Are there Machine Learning Models for Networks?
Coming from the related field of measuring and predicting network security, I'd strongly suggest trying a time-series forecasting. I assume your data is time-stamp based (network congestion values, sampled at some interval? if not skip to the second idea)

1st idea:
I'd borrow from time-series the concept of flattening the 100 measurements into 1 datum.
So instead of:

[t1+delta1, location1, messurement1]

[t1+delta2, location2, messurement2]

[t1+delta3, location3, messurement3]

Fold into:

[t1-bucketed,  loc1, mess1, loc2, mess2, loc3, mess3.]

This would help the model "grasp" the relation between the different measurements, with emphasis on the time axis

2nd idea
Flatten into each measurement row the closest measurements by topology (or even the entire 100 neighbors) into:

[mess, topol-1-mess, topol-1-dist, topol-1-other, topol-2-mess, topol-2-dist, topol-2-other, ..]

This would help the model "grasp" the relation between a specific measurement and its neighborhood measurements, with emphasis on the topology features from each measurement

Please let us know if that helped :)