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and what are the similar rules that govern how they operate how they affect our lives <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-65.4-70.36-<NA> | [
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but recently we 've been wondering whether it might be possible to take advantage of this insight to actually find ways to improve the world to do something better to actually fix things | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-69.9-79.94-<NA> | [
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<unk> not just understand things | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-79.9-81.98-<NA> | [
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so one of the first things we thought we would tackle would be how we go about predicting epidemics <unk> | NicholasChristakis_2010S | -1no label
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and the current state of the art in predicting an epidemic if you 're the cdc or some other national body | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-88-93.6-<NA> | [
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is to sit in the middle where you are and collect data from physicians and laboratories in the field that report the prevalence or the incidence of certain conditions so so and so patients have been diagnosed with something | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-93.41-104.97-<NA> | [
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<unk> or other patients have been diagnosed and all these data are fed into a central repository with some delay <unk> and if everything goes smoothly one to two weeks from now you 'll know where the epidemic was today | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-104.96-116.52-<NA> | [
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and actually about a year or so ago there was this promulgation of the idea of google flu trends with respect to the flu where | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-117.14-125.02-<NA> | [
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<unk> by looking at people 's searching behavior today <unk> we could know where the flu what the status of the epidemic was today what 's the prevalence of the <unk> <unk> of the epidemic today <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-125.19-134.56-<NA> | [
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] | 9.373333 |
but what i 'd like to show you today is a means by which we might get not just rapid warning about an epidemic <unk> but also actually early detection of an epidemic and | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-134.1-145.33-<NA> | [
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] | 11.24 |
in fact this idea can be used not just to predict epidemics of germs <unk> but | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-144.84-149.93-<NA> | [
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<unk> to predict epidemics of all sorts of kinds <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-150-153.47-<NA> | [
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for example anything that spreads by a form of social contagion could be understood in this way from abstract ideas on the left like patriotism <unk> or altruism <unk> or <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-153.43-163.61-<NA> | [
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<unk> religion to practices like dieting behavior or book purchasing or drinking or bicycle helmet [ and ] other safety practices <unk> or products that people might buy purchases of electronic | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-163.51-174.87-<NA> | [
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<unk> anything in which there 's kind of an interpersonal spread a kind of a diffusion of innovation could be understood and predicted by the mechanism i 'm going to show you now | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-175.58-184.91-<NA> | [
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so as all of you probably know | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-184.91-187.41-<NA> | [
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the classic way of thinking about this is the <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-187.07-189.84-<NA> | [
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<unk> the adoption curve <unk> so here on the y axis we have the percent of the people affected <unk> and on the x axis we have time and at the very beginning not too many people are | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-190.91-199.78-<NA> | [
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283,
804,
249,
2351,
625,
2346,
1381,
2087,
2221,
3818,
2590,
3703,
1728,
357,
2608,
2369,
3459,
1603,
2191,
3150,
1220,
3991,
2163,
1369,
1748,
4095,
2414,
845,
2402,
3104,
772,
2516,
536,
2014,
397,
2934,
3279,
2997,
854,
785,
2207,
1175,
2082,
1988,
4092,
2274,
2799,
3326,
71,
3622,
3974,
432,
3170,
2258,
3585,
466,
3705,
1792,
1423,
3477,
1823,
3404,
2195,
2212,
627,
2431,
1809,
3653,
2994,
3476,
1908,
3632,
2614,
4061,
1827,
1313,
3786,
1291,
2599,
3656,
3535,
249,
2936,
2033,
407,
3078,
423,
1843,
1056,
3160,
1390,
800,
2805,
2817,
3599,
2577,
3385,
2733,
1386,
3644,
1695,
441,
3189,
1433,
966,
30,
4023,
1065,
588,
227,
1855,
679,
3486,
1955,
4015,
2650,
1313,
2,
3178,
781,
94,
1700,
3739,
1119,
586,
1447,
1951,
3739,
2226,
586,
3864,
3678,
3433,
4014,
3769,
60,
3921,
2906,
2378,
2237,
2166,
2760,
496,
3155,
3336,
1941,
1115,
3802,
2542,
1867,
586,
1497,
2696,
705,
3720,
1676,
2743,
572,
269,
1613,
2887,
2680,
3781,
3226,
293,
833,
1383,
1912,
1759,
3533,
1870,
3600,
1848,
109,
2214,
1531,
2195,
3505,
2581,
1447,
3354,
2707,
521,
533,
933,
3095,
2614,
676,
2129,
3563,
2167,
1755,
2608,
281,
1322,
3445,
3193,
1801,
3623,
3600,
1956,
1386,
2485,
1563,
2401,
4043,
4009,
2066,
2909,
4087,
1207,
2629,
1252,
2605,
1465,
3624,
3483,
3129,
390,
3422,
650,
3461,
3487,
3091,
1097,
3586,
1588,
3813,
2318,
2997,
4082,
2716,
1096,
2403,
1983,
2316,
1618,
1839,
1017,
2111,
521,
1419,
1431,
1690,
2137,
2928,
107,
2033,
815,
671,
1002,
1185,
2564,
2479,
1984,
4083,
2378,
276,
3687,
516,
1997,
907,
3589,
3421,
654,
2205,
781,
1406,
1313,
2542,
2073,
3543,
3739,
3687,
692,
1484,
2997,
2310,
3687,
692,
1873,
2997,
1946,
2372,
1865,
2436,
3705,
2906,
3277,
1500,
1315,
768,
3849,
3276,
3087,
1163,
2992,
1169,
2743,
3194,
988,
2731,
31,
1327,
2383,
3163,
2800,
2188,
2932,
2295,
1991,
3656,
529,
305,
91,
1419,
1,
360,
3748,
1574,
2396,
1969,
3042,
2621,
1190,
1496,
1233,
998,
568,
3388,
3449,
691,
2868,
1695,
3777,
1900,
1616,
286,
3167,
495,
1697,
433,
2106,
1166,
1364,
971,
1623,
3970,
2022,
918,
1029,
2976,
435,
42,
1844,
382,
1515,
3481,
3289,
689,
889,
2025,
2769,
572,
3412,
802,
2766,
232,
1841,
1648,
1982,
1397,
1844,
1729,
731,
1004,
3482,
2089,
555,
1402,
2436,
3737,
2957,
412,
2384,
232,
1765,
3252,
2052,
1041,
716,
1263,
705,
3262,
1742,
217,
223,
2931,
1775,
1198,
441,
600,
4043,
260,
1663,
1339,
2984,
1400,
1795,
2846,
2383,
4092,
726,
1550,
4080,
3429,
3622,
337,
3769,
1114,
1509,
3517,
372,
1008,
3379,
1523,
3772,
2052,
450,
930,
930,
1844,
4027,
3566,
218,
1821,
601,
1771
]
]
] | 8.88 |
<unk> and you get this classic sigmoidal or s shaped curve <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-200.46-203.72-<NA> | [
[
[
3757,
2584,
2542,
2855,
2016,
1904,
2207,
414,
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2378,
1256,
1260,
4079,
3174,
670,
2282,
2303,
1855,
2743,
3562,
3226,
2061,
1212,
187,
3090,
2266,
3449,
3134,
1383,
2100,
1582,
3091,
1217,
3321,
3490,
148,
3831,
1801,
1803,
1410,
1808,
240,
2730,
3571,
3749,
3810,
22,
3777,
89,
2840,
1189,
2312,
65,
1519,
896,
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3785,
145,
2973,
1828,
2742,
2641,
3225,
2413,
1691,
840,
3523,
3119,
1020,
162,
1213,
50,
2283,
2808,
851,
2166,
3457,
1864,
522,
3068,
344,
944,
3732,
488,
3490,
3768,
1801,
2351,
3120,
3533,
2370,
4012,
3975,
2799,
1133,
3495,
2380,
522,
2385,
602,
2929,
2755,
1710,
516,
2999,
1101,
2175,
4020,
3592,
3113,
3007,
1340,
2888,
640,
4035,
3323,
1929,
2717,
756,
2309,
2006,
716,
2932,
907,
474,
2535,
2632,
3632,
1163,
3679,
601,
4012,
235,
3527,
1380,
3648,
340,
3827,
4076,
3624,
17,
2864,
2281,
2369,
2372,
975,
3844,
2695,
572,
2549,
2144,
29,
3043,
1447,
706,
3506,
1897,
1525,
4018,
1637,
3375,
33,
3546,
344,
666,
759,
3600,
1011,
2798,
2358,
2804,
966,
277,
1654,
920,
1116,
2512,
1909,
2765,
1389,
3134,
1951,
2997,
3586,
2512,
3518,
4080,
1313,
586,
3178,
692,
2129,
1234,
348,
1919,
1397,
3053,
2849,
3384,
3200,
3477,
340,
3531,
1585,
1750,
1152,
418,
2765,
1227,
3388,
3642,
1105,
2682,
74,
909,
207,
3067,
1119,
3786,
1885,
483,
3571,
1867,
3711,
3303,
2997,
2536,
62,
586,
3439,
3726,
2235,
2479,
586,
4083,
2479,
708,
2073,
3543,
3122,
2757,
1865,
2548
]
]
] | 3.266667 |
and the reason for this shape is that at the very beginning let 's say one or two people are infected or affected by the thing and then they affect or infect two people who in turn affect four eight sixteen and so forth <unk> and you get the epidemic growth | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-203.72-216.77-<NA> | [
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3852,
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637,
2980,
3982,
606,
3763,
2888,
340,
1111,
1708,
1963,
3307,
3346,
1430,
2006,
787,
3552,
559,
30,
1101,
1474,
1183,
3199,
3060,
2687,
2346,
1450,
54,
1920,
2063,
1499,
779,
2395,
2249,
1306,
625,
1387,
2206,
2529,
577,
3707,
1385,
440,
2411,
712,
3151,
632,
2781,
3454,
646,
4013,
2239,
726,
3737,
2513,
3091,
2664,
3600,
1930,
1410,
625,
1525,
555,
116,
1242,
1755,
1811,
3495,
1113,
1699,
2383,
495,
3315,
180,
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1011,
3823,
820,
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2694,
3178,
1146,
1082,
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4032,
4051,
55,
1826,
3945,
661,
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2907,
2226,
2997,
1152,
2226,
3797,
1969,
2496,
3391,
3726,
1926,
3583,
1339,
59,
2560,
3148,
1863,
126,
1044,
419,
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2206,
1009,
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3364,
3842,
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3479,
2359,
3880,
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952,
3847,
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2569,
3666,
1198,
2094,
2819,
194,
946,
2525,
1663,
2387,
1859,
2556,
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1568,
58,
1955,
2067,
3031,
801,
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3428,
697,
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356,
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2092,
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2440,
740,
2070,
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3954,
2503,
2472,
203,
1720,
658,
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2589,
2496,
1969,
1801,
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3523,
1054,
3651,
2702,
1244,
2589,
595,
3456,
1654,
712,
3917,
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3433,
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1555,
3670,
227,
3506,
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3484,
1038,
3176,
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3505,
2612,
1913,
2499,
1768,
390,
779,
3829,
3833,
38,
2589,
4068,
1618,
2201,
3954,
1869,
924,
3822,
1119,
2290,
2853,
3452,
3991,
2017,
561,
2053,
1942,
2264,
2881,
1730,
207,
1826,
3283,
1345,
3309,
2556,
3768,
3935,
4081,
2175,
380,
198,
3755,
1663,
4095,
3712,
2703,
4038,
1163,
1804,
1957,
2689,
95,
1981,
1929,
2286,
3046,
937,
711,
481,
2003,
1850,
2846,
1526,
2508,
1430,
2313,
2783,
688,
3945,
2537,
1997,
1406,
854,
3891,
3789,
1997,
2333,
3593,
1313,
1120,
2984,
446,
1332,
568,
1650,
612,
1725,
1271,
1337,
3235,
3101,
2023,
1410,
3884,
2033,
3156,
1934,
683,
2318,
2401,
3336,
462,
982,
3043,
3391,
3093,
2803,
3415,
145,
3819,
3981,
3844,
2479,
3443,
2615,
2724,
1,
3381,
800,
2913,
3314,
2911,
2494,
1984,
851,
353,
2402,
1854,
3330,
1025,
2114,
1869,
579,
389,
2242,
3407,
3997,
266,
2484,
2997,
3786,
3742,
3880,
3156,
1885,
2255,
369,
3858,
1006,
2829,
460,
1102,
1512,
3295,
2069,
436,
1821,
860,
447,
3687,
498,
976,
1207,
114,
2366,
3147,
1353,
1194,
1207,
3641,
2895,
954,
3821,
3036,
2663,
3684,
3157,
21,
3075,
3274,
2380,
4012,
3844,
2928,
95,
744,
663,
842,
405,
429,
4066,
249,
3290,
844,
3488,
3449,
2646,
1209,
516,
2506,
2292,
71,
1390,
1873,
398,
1029,
2296,
3486,
605,
2800,
2650,
504,
906,
1604,
3219,
744,
2822,
2755,
2296,
2525,
1758,
1388,
2646,
3205,
459,
3397,
1885,
1811,
3169,
2972,
692,
1901,
1370,
3057,
2688,
2572,
1700,
1351,
905,
3917,
1975,
939,
1613,
3635,
1176,
3851,
3025,
630,
3584,
1121,
3438,
2135,
160,
853,
2932,
3030,
301,
1260,
3777,
1969,
1479,
2968,
1657,
1458,
3953,
3228,
3322,
3729,
836,
2802,
1555,
3964,
1062,
740,
2174,
1292,
1118,
3452,
3518,
1956,
1573,
2646,
2294,
634,
2710,
3894,
2206,
3141,
2445,
3851,
2920,
3304,
1331,
2945,
3153,
1593,
436,
1825,
3496,
2952,
816,
570,
2936,
468,
1725,
3946,
434,
3504,
1337,
1495,
2851,
351,
3454,
840,
249,
1160,
3873,
3711,
247,
799,
1737,
2099,
3455,
3450,
1078,
3042,
1573,
1679,
1003,
317,
4088,
2798,
3844,
3555,
2800,
3668,
1664,
728,
1842,
800,
1785,
1826,
559,
3980,
568,
1421,
993,
2772,
1889,
738,
1997,
12,
32,
3220,
698,
2516,
2893,
2651,
1695,
1813,
153,
1910,
504,
887,
3974,
380,
2027,
2401,
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1363,
3894,
2907,
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2583,
146,
3878,
407,
3278,
4024,
3822,
1339,
3571,
1528,
70,
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2757,
3715,
1885,
2907,
3571,
3726,
2416,
1439,
3726,
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3278,
1142,
3392,
3753,
1450,
2515,
435,
1262,
202,
2436,
1264,
3105,
3040,
3134,
344,
1958,
1121,
759,
2503,
2347,
1912,
3143,
2197,
529,
2242,
407,
608,
2883,
1258,
497,
2104,
2221,
952,
3714,
601,
1291,
3488,
3122,
3675,
3838,
1759,
3777,
3242,
3043,
851,
1147,
1265,
3505,
750,
1441,
1904,
2577,
567,
3829,
483,
3799,
274,
3488,
2413,
3175,
1076,
2462,
249,
840,
2761,
2496,
2078,
2701,
3280,
1422,
791,
1460,
1981,
2668,
411,
3604,
1177,
2608,
2668,
3194,
3225,
2414,
2889,
1740,
257,
3536,
306,
1969,
1633,
1872,
3906,
3824,
120,
3035,
547,
720,
3754,
3326,
3753,
3153,
1292,
106,
1695,
780,
4087,
3424,
1988,
3970,
2075,
1357,
2410,
1574,
1055,
2819,
63,
1381,
3472,
1332,
1573,
1683,
4051,
3488,
3207,
3711,
2160,
625,
3523,
3207,
3523,
2254,
2218,
816,
1306,
2011,
3175,
1946,
2236,
3369,
2461,
2167,
4084,
3021,
1395,
654,
1153,
2099,
1988,
325,
1011,
482,
446,
1380,
870,
3876,
764,
2396,
3181,
4044,
1311,
2109,
3120,
3533,
1755,
3523,
2351,
3600,
1397,
3130,
2601,
3132,
2297,
3744,
1346,
2543,
1397,
234,
3518,
3740,
1795,
3072,
2914,
1095,
3433,
2371,
526,
3603,
2431,
2755,
4035,
3851,
1775,
867,
2175,
692,
69,
3842,
2404,
3555,
70,
3753,
3974,
3715,
2226,
436,
554,
3687,
3280,
2073,
3543,
1265,
3687,
708,
1885,
2800,
2402,
2368,
134,
3765,
2517,
691,
1261,
3970,
3205,
1543,
607,
2571,
1384,
2667,
2689,
3771,
3178,
1795,
1859,
2649,
3406,
1761,
3118,
2090,
1683,
990,
2745,
3279,
2100,
3633,
687,
1240,
1115,
1825,
919,
984,
2496,
3295,
1017,
2735,
3139,
3145,
2245,
660,
1452,
1967,
3358,
4062,
228,
2983,
902,
511,
380,
1194,
3650,
3866,
1110,
2862,
3662,
2270,
974,
5,
1652,
1804,
951,
989,
1475,
3344,
2059,
2309,
1931,
3385,
2798,
254,
3166,
1119,
3893,
1593,
2815,
772,
418,
396,
2837,
1384,
2009,
2406,
1023,
273,
200,
723,
1322,
1569,
1283,
3488,
888,
3711,
3813,
2835,
4034,
794,
3175,
944,
2109,
3758,
477,
3021,
3516,
2479,
2016,
314,
2584,
1078,
2376,
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phase of the curve <unk> and eventually you saturate the population there are fewer and fewer people who are still available that you might infect and then you get the plateau of the curve <unk> and you get this classic sigmoidal curve <unk> and this holds for germs | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-216.59-229.94-<NA> | [
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<unk> ideas product adoption behaviors <unk> and the like | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-230.31-234.86-<NA> | [
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but things don 't just diffuse in human populations at random they actually diffuse through networks <unk> because as i said we live our lives in networks and these networks have a particular kind of a structure | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-234.52-246.23-<NA> | [
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now if you look at a network like this this is one hundred and five people and the | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-246.22-249.87-<NA> | [
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<unk> lines represent the dots are the people and the lines represent friendship relationships you might see that people occupy different locations within the network | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-250-258.01-<NA> | [
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and there are different kinds of relationships between the people you could have friendship relationships sibling relationships <unk> spousal relationships co worker relationships <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-257.77-266.45-<NA> | [
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neighbor relationships and the like | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-266.45-269.32-<NA> | [
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and different sorts of things spread across different sorts of ties <unk> for instance sexually transmitted diseases will spread across sexual ties <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-269.09-276.88-<NA> | [
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or <unk> for instance people 's smoking behavior might be influenced by their | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-276.64-279.94-<NA> | [
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<unk> friends <unk> or their altruistic or their charitable giving behavior might be influenced by their coworkers or by their neighbors <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-279.87-287.03-<NA> | [
[
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3760,
1239,
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3822,
1795,
4030,
1662,
3156,
1899,
1370,
1076,
673,
3392,
3860,
2177,
529,
3414,
1198,
1326,
3763,
4026,
3132,
3800,
2715,
663,
3445,
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2765,
227,
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677,
3931,
744,
493,
59,
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2972,
1408,
1311,
1801,
109,
3523,
2575,
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751,
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3027,
3734,
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2205,
3397,
1331,
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2404,
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1048,
1162,
3726,
1912,
4091,
3694,
374,
3705,
69,
541,
3726,
2674,
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2860,
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3223,
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2043,
1321,
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3480,
1792,
1492,
3802,
3975,
1271,
2111,
1321,
658,
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1419,
3772,
533,
2628,
231,
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616,
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348,
322,
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189,
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290,
104,
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857,
3891,
1035,
3483,
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2772,
2205,
620,
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1209,
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256,
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1269,
3552,
3274,
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2524,
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3871,
963,
1723,
1748,
54,
262,
1471,
2265,
2048,
3930,
2997,
678,
2508,
2467,
1539,
1433,
2419,
2853,
3015,
1702,
1807,
1859,
59,
1491,
3694,
1440,
1264,
2001,
3845,
339,
2884,
3115,
4035,
1131,
3876,
1679,
1431,
337,
1758,
1530,
586,
1593,
1313,
692,
1573,
483,
3571,
2757,
3786,
1573,
3175,
69,
2479,
2235,
3326,
1313,
1955,
3801,
2412,
2122,
2010,
1769,
2549,
936,
2270,
1987,
2073,
3387,
2073,
3880,
2331,
3183,
3650,
3634,
2550,
4028,
1415,
1829,
334,
3592,
1926,
837,
2183,
802,
888,
1386,
2255,
813,
625,
3638,
2693,
2706,
2851,
1526,
2457,
3603,
432,
4010,
486,
1618,
2024,
1890,
765,
227,
1731,
2275,
751,
1997,
3705,
1563,
821,
2529,
3345,
3052,
1277,
2929,
415,
2834,
2124,
2835,
3142,
873,
2861,
3023,
3851,
114,
4090,
1449,
4034,
225,
854,
2,
2220,
4082,
2,
1811,
619,
1641,
3525,
3323,
2332,
2683,
2161,
3955,
2529,
4068,
3459,
1417,
2564,
3307,
549,
2037,
2175,
2317,
340,
516,
22,
642,
2693,
1553,
1997,
2631,
2205,
2033,
363,
2992,
428,
3400,
1860,
1132,
392,
2373,
492,
855,
2804,
222,
802,
3372,
3120,
1076,
981,
3711,
2351,
2599,
3479,
3891,
3726,
720,
1855,
2907,
1432,
3974,
1758,
2205,
586,
1593,
2425,
2235,
1386,
522,
1406,
3705,
2235,
2479,
128,
1126,
397,
2564,
857,
2632,
1956,
3912,
3985,
3641,
2498,
19,
3640,
70,
3527,
3718,
1537,
362,
1024,
3065,
3895,
212,
4057,
2506,
1495,
779,
3283,
2144,
1715,
2831,
2573,
1753,
1506,
3157,
124,
2012,
2017,
3945,
2430,
679,
3495,
245,
3507,
2335,
1544,
841,
2531,
697,
2672,
106,
3650,
4087,
1102,
2075,
2673,
1182,
1035,
1371,
571,
2244,
2363,
1132,
2147,
1216,
1244,
3125,
3096,
2067,
1683,
70,
1311,
2213,
884,
3711,
4077,
884,
2160,
2148,
1137,
3253,
1833,
2286,
1079,
3726,
1432,
3421,
781,
2205,
1351,
2235,
1313,
522,
3187,
3421,
794,
3583,
522,
975,
3421,
3694,
1530,
522,
519,
2991
]
]
] | 7.16 |
but not all positions in the network are the same so if you look at this you might immediately grasp that different people have different numbers of connections <unk> some people have one connection some have two some have six some have ten connections <unk> and this is | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-287.03-300.05-<NA> | [
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3450,
482,
1906,
1055,
3136,
1432,
2972,
1563
]
]
] | 13.026667 |
<unk> of a node or the number of connections that a node has | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-300.94-304.43-<NA> | [
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] | 3.493333 |
but in addition there 's something else <unk> so if you look at nodes a and b they both have six connections <unk> but if you can see this image [ of the network ] from a bird 's eye view you can appreciate that there 's something very different about nodes a and <unk> b | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-304-316.99-<NA> | [
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] | 13 |
so let me ask you this i can cultivate this intuition by asking a question who would you rather be if a deadly germ was spreading through the network <unk> a or | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-316.99-324.99-<NA> | [
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<unk> it 's obvious <unk> b is located on the edge of the network now who would you rather be if a juicy piece of gossip were spreading through the network <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-326.72-334.59-<NA> | [
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a and you have an immediate appreciation that a is going to be more likely to get the thing that 's spreading and to get it sooner by virtue of their structural location <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-334.49-344.43-<NA> | [
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within the network <unk> a in fact is more central <unk> and this can be formalized mathematically <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-344.38-350.28-<NA> | [
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<unk> we ideally would like to do is to set up sensors on the central individuals within the network <unk> including node a monitor those people that are right there in the middle of the network and somehow get an early <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-355.02-365.14-<NA> | [
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<unk> detection of whatever it is that is spreading through the network <unk> so if you saw them contract a germ or a piece of information you would know that soon enough everybody was about to contract this germ or this piece of information <unk> and this would be much better | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-365.04-379.64-<NA> | [
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<unk> monitoring six randomly chosen people <unk> without reference to the structure of the population and | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-379.81-385.46-<NA> | [
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in fact if you could do that what you would see is something like this on the left hand panel again we have the s shaped curve of adoption in the dotted red line we show what the adoption would be in the <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-385.39-395.48-<NA> | [
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] | 10.093333 |
<unk> random people and in the left hand line <unk> shifted to the left <unk> we show what the adoption would be in the central individuals within the network <unk> on the | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-395.38-403.08-<NA> | [
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] | 7.706667 |
<unk> contagion and on the x axis is the time and on the right hand side we show the same data <unk> but here with daily incidence and what we show here is like here very few people are affected more and more and more and up to here <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-404.72-415.02-<NA> | [
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]
]
] | 10.306667 |
and here 's the peak of the epidemic <unk> but shifted to the left is what 's occurring in the central individuals <unk> and this difference in time between the two is the early detection the early warning we can get about an impending epidemic in the human population | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-414.97-429.18-<NA> | [
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<unk> however is that mapping human social networks is not always possible | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-429.93-434.99-<NA> | [
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it can be expensive not feasible <unk> unethical or frankly <unk> just not possible to do such a thing | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-434.99-442.51-<NA> | [
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so how can we figure out who the central people are in a network without actually mapping the network <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-442.32-448.96-<NA> | [
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what we came up with was an idea to exploit an old fact or a known fact about social networks | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-448.74-454.76-<NA> | [
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<unk> which goes like this do you know that your friends have more friends than you do | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-454.9-461-<NA> | [
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your friends have more friends than you do and | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-461.14-464.87-<NA> | [
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] | 3.733333 |
this is known as the friendship paradox imagine a very popular person in the social network like a party host who has hundreds of friends and a misanthrope who has just one friend <unk> and you pick someone | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-464.5-475.14-<NA> | [
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] | 10.64 |
<unk> at random from the population <unk> they were much more likely to know the party host and if they nominate the party host as their friend that party host has a hundred friends <unk> therefore has more friends than they do <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-475.1-486.27-<NA> | [
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and this in essence is what 's known as the friendship paradox the friends of randomly chosen people have higher degree <unk> and are more central than the random people themselves <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-485.9-496.5-<NA> | [
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<unk> and you can get an intuitive appreciation for this if you imagine just the people at the perimeter of the network <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-496.45-501.67-<NA> | [
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<unk> person who by construction must have at least two and typically more friends | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-505.05-510.28-<NA> | [
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]
] | 5.24 |
and that happens at every peripheral node <unk> and in fact it happens throughout the network as you move in everyone you pick when they nominate a random when a random person nominates a friend of theirs you move closer to the center of the network <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-509.85-523.09-<NA> | [
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]
]
] | 13.24 |
so <unk> we thought we would exploit this idea in order to study whether we could predict phenomena within networks | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-522.75-529.43-<NA> | [
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] | 6.68 |
<unk> now with this idea we can take a random sample of people have them nominate their friends those friends would be more central and we could do this without having to map the network <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-529.9-539.76-<NA> | [
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and we tested this idea with an outbreak of h1n1 flu at harvard college | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-539.76-545.37-<NA> | [
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in the fall and winter of two thousand and nine just a few months ago <unk> we took one thousand three hundred randomly selected | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-545.16-550.17-<NA> | [
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]
] | 5.013333 |
<unk> we had them nominate their friends and we followed both the random students and their friends daily in time to see whether or not they had the flu epidemic <unk> and we did this passively by looking <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-551.12-561.45-<NA> | [
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1428,
3225,
2010,
3055,
1227,
2113,
4035,
1898,
2626,
2847,
993,
1052,
3985,
3383,
2014,
764,
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3635,
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954,
1833,
3101,
3709,
1754,
1166,
1709,
298,
1867,
2266,
2205,
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1102,
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1601,
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1983,
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1584,
1313,
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344,
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3675,
350,
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3118,
2947,
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1995,
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3347,
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3954,
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3714,
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2609,
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646,
1795,
335,
276,
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520,
2462,
3946,
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17,
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906,
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3427,
297,
171,
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2304,
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383,
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3462,
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1740,
3075,
3218,
1924,
925,
2022,
3489,
3125,
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2804,
3178,
3219,
153,
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3821,
915,
1217,
930,
3724,
3407,
2593,
166,
529,
598,
2646,
1929,
889,
668,
1762,
2009,
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3390,
153,
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306,
2268,
539,
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3115,
3861,
3666,
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277,
2859,
2948,
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3980,
520,
306,
2214,
2612,
443,
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1746,
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3086,
3689,
2928,
1077,
3842,
4055,
1847,
1558,
1648,
1530,
1048,
4083,
3726,
3715,
2073,
2997,
1933,
3326,
2542,
1530,
2016,
276,
3421,
781,
1319,
3066,
1660,
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574,
427,
2691,
801,
1156,
1049,
1096,
3925,
2077,
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3562,
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2714,
274,
3711,
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3797,
1272,
1238,
3930,
372,
292,
3118,
3073,
3973,
372,
801,
712,
83,
618,
1737,
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2980,
2090,
1304,
2616,
1417,
2430,
790,
3483,
2281,
1893,
3716,
2584,
1068,
4008,
2972,
1844,
1855,
2235,
2205,
207,
1844,
1967,
2243,
1439,
519,
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2548,
3422,
1721,
1936,
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1951,
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3739,
1969,
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2742,
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207,
3618,
1630,
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<unk> at whether or not they 'd gone to university health services and also we had them [ actively ] email us a couple of times a week | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-561.35-566.71-<NA> | [
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<unk> and the difference in the two is sixteen days by monitoring the friends group we could get sixteen days advance warning of an impending epidemic in this human population | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-575.4-585.25-<NA> | [
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now | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-584.79-586.1-<NA> | [
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in addition to that if you were an analyst who was trying to study an epidemic or to predict the adoption of a product for example what you could do is you could pick a random sample of the population <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-585.86-596.24-<NA> | [
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also have them nominate their friends and follow the friends and follow both the randoms and the friends among the friends the first evidence you saw of a blip | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-596.24-604.88-<NA> | [
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<unk> above zero in adoption of the innovation for example would be evidence of an impending epidemic | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-604.84-610.4-<NA> | [
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or you could see the first time the two curves diverged as shown on the left when did the randoms when did the friends take off and leave the randoms and [ when did ] | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-610.4-620.67-<NA> | [
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their curve start shifting and that as indicated by the white line <unk> occurred | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-620.43-624.99-<NA> | [
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<unk> before the peak of the epidemic so this would be a technique whereby we could get more than a month and a half warning about a flu epidemic in a particular population | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-626.21-635.16-<NA> | [
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i should say that how far advanced a notice one might get about something depends on a host of factors it could depend on the nature of the pathogen different pathogens <unk> using <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-634.91-645-<NA> | [
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<unk> this technique you 'd get different warning or other phenomena that are spreading <unk> or frankly on the structure of the human network <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-644.9-652.09-<NA> | [
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] | 7.2 |
<unk> could also actually map the network of the students so this is a map of seven hundred and fourteen students and their friendship ties <unk> and in a minute now i 'm going to put this map into motion we 're going to take daily cuts through the network for one hundred and twenty days | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-654.9-666.74-<NA> | [
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] | 11.84 |
the red dots are going to be cases of the flu <unk> and the yellow dots are going to be friends of the people with the flu <unk> and the size of the dots is going to be proportional to <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-666.74-675.67-<NA> | [
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] | 8.933333 |
how many of their friends have the flu <unk> so bigger dots mean more of your friends have the flu | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-675.3-679.85-<NA> | [
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and if you look at this image here we are now in september the 13th you 're going to see a few cases light up you 're going to see kind of blooming of the flu in the middle here we are on october the 19th the slope <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-679.86-690.2-<NA> | [
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<unk> of the epidemic curve is approaching now in november bang bang bang bang bang you 're going to see lots of blooming in the middle and then you 're going to see a sort of leveling off fewer and fewer cases towards the end of december <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-690.1-699.96-<NA> | [
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<unk> individuals first before they affect others | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-705.2-708.42-<NA> | [
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now as i 've been suggesting this method is not restricted to germs but actually to anything that spreads in populations | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-708.14-715.39-<NA> | [
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information spreads in populations <unk> norms can spread in populations behaviors can spread in populations <unk> and by behaviors i can mean things like criminal behavior <unk> or voting | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-715.39-724.75-<NA> | [
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2166,
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2757,
692,
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3385,
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1162
]
]
] | 9.36 |
<unk> or health care behavior like smoking or vaccination <unk> or product adoption or other kinds of behaviors that relate to interpersonal influence <unk> if i 'm likely to do something that affects others around <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-725.43-735.82-<NA> | [
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]
] | 10.4 |
<unk> me this technique can get early warning or early detection about the adoption within the population <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-735.72-741.91-<NA> | [
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2730,
335,
1855,
522,
473,
3415,
335,
2688
]
]
] | 6.2 |
<unk> the same insights can also be exploited with respect to networks can also be exploited in other ways <unk> for example in the use of targeting specific people for interventions <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-753.22-764.57-<NA> | [
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] | 11.36 |
so for example most of you are probably familiar with the notion of herd immunity <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-764.08-768.36-<NA> | [
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1386,
3259,
794,
1339,
335,
1292
]
]
] | 4.28 |
so if we have a population of a thousand people and we want to make the population immune to a pathogen we don 't have to immunize | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-768.36-774.87-<NA> | [
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<unk> every single person <unk> if we immunize nine hundred and sixty of them it 's as as if we had immunized a hundred [ percent ] of them <unk> because even if one or two of the non immune people gets infected there 's no one for them to <unk> | NicholasChristakis_2010S | -1no label
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<unk> infect they are surrounded by immunized people so ninety six percent is as good as one hundred percent well | NicholasChristakis_2010S | -1no label
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<unk> and the answer is no <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-800.58-802.18-<NA> | [
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but if you took this thirty percent these three hundred people and had them nominate their friends and took the same number of vaccine doses and vaccinated the friends of | NicholasChristakis_2010S | -1no label
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<unk> the three hundred the three hundred friends you can get the same level of herd immunity as if you had vaccinated ninety six percent of the population at a much greater efficiency with a strict budget constraint | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-811.68-822.44-<NA> | [
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] | 10.76 |
in villages we could target to whom to give the interventions to foster these kinds of spreads or frankly for advertising with all kinds of products if we could understand how to target it could affect the efficiency of what we 're trying to achieve <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-829.92-844.07-<NA> | [
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] | 14.16 |
and in fact we can use data from all kinds of sources nowadays [ to do this ] this is a map of eight million phone users in a european country every dot is a person and every line represents a volume of | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-844.21-854.88-<NA> | [
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] | 10.68 |
<unk> calls between the people and we can use such data that 's being passively obtained to map these whole countries and understand who is located where within the network without actually <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-854.84-864.96-<NA> | [
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]
] | 10.12 |
<unk> having to query them at all we can get this kind of a structural insight and other sources of information as you 're no doubt aware are available about such features from email interactions online | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-864.86-874.84-<NA> | [
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] | 9.986667 |
<unk> online social networks and so forth <unk> and in fact we are in the era of what i would call <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-875.85-881.38-<NA> | [
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<unk> data collection efforts they 're all kinds of ways we can use massively collected data | NicholasChristakis_2010S | -1no label
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to create sensor networks to follow the population <unk> understand what 's happening in the population <unk> and intervene in the population for the better <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-887.3-897.07-<NA> | [
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because these new technologies tell us not just who is talking to whom but where everyone is and what they 're thinking based on what they 're uploading | NicholasChristakis_2010S | -1no label
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<unk> on the internet <unk> and what they 're buying based on their purchases <unk> and all this administrative data can be pulled together and processed to understand human behavior in a way we never could before <unk> | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-904.9-916.07-<NA> | [
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so for example we could use truckers ' purchases of fuel <unk> so the truckers are just going about their business and they 're buying fuel <unk> and we see a blip up in the truckers | NicholasChristakis_2010S | -1no label
| /home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/eedc9aee02a9a4bc4086c93dceb40e9614e69c56ab990916816aef2b627d8a53/train/NicholasChristakis_2010S.sph | NicholasChristakis_2010S-915.91-925.17-<NA> | [
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2141,
1727,
2024,
2104,
2775,
955,
2191,
1795,
2071,
785,
3711,
462,
3175,
3156,
1885,
2763,
1898,
344,
1736,
1901,
1795,
1190,
3931,
2123,
3478,
1535,
3786,
418,
1363,
857,
3638,
873,
3148,
3307,
994,
2884,
4073,
2595,
128,
2693,
3726,
1912,
2408,
3219,
1464,
2106,
1922,
586,
198,
3726,
94,
198,
2416,
1439,
2997,
3571,
1700,
1152,
1842,
802,
317,
2425,
3454,
2391,
2997,
1440,
3154,
1505,
2855,
3726,
905,
721,
2569,
4072,
1158,
3189,
275,
1325,
2380,
1046,
3737,
1973,
54,
271,
1078,
2734,
1495,
3309,
2517,
3795,
488,
2351,
625,
751,
2010,
4003,
297,
3174,
990,
1913,
624,
1755,
3093,
1826,
1259,
1566,
2166,
2016,
1167,
34,
2485,
2618,
83,
1723,
2016,
3749,
2496,
3496,
3367,
1480,
1808,
3865,
2188,
3190,
895,
3984,
241,
2757,
1406,
448,
95,
220,
1048,
2192,
1871,
836,
533,
931,
3354,
3577,
3883,
2587,
2936,
1679,
1530,
4088,
247,
3010,
3812,
3266,
1990,
1888,
1917,
2938,
2012,
1894,
3141,
2283,
2808,
554,
2589,
3453,
927,
3789,
1449,
3488,
3600,
1969,
2399,
1466,
4030,
783,
72,
3269,
2676,
1923,
2772,
2191,
2083,
74,
906,
407,
2201,
3266,
498,
1666,
556,
1126,
2906,
2662
]
]
] | 9.266667 |
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