Changes in the structure of observed social and complex networks' structure can indicate a significant underlying change in an organization, or reflect the response of the network to an external event. Automatic detection of change points in evolving networks is rudimentary to the research and the understanding of the effect of such events on networks. Here we present an easy-to-implement and fast framework for change point detection in temporal evolving networks. Unlike previous approaches, our method is size agnostic, and does not require either prior knowledge about the network's size and structure, nor does it require obtaining historical information or nodal identities over time. We use both synthetic data derived from dynamic models and two real datasets: Enron email exchange and Ask-Ubuntu forum. Our framework succeeds with both precision and recall and outperforms previous solutions
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@cite_41: 
@cite_36: We live life in the network. When we wake up in the morning, we check our e-mail, make a quick phone call, walk outside (our movements captured by a high definition video camera), get on the bus (swiping our RFID mass transit cards) or drive (using a transponder to zip through the tolls). We arrive at the airport, making sure to purchase a sandwich with a credit card before boarding the plane, and check our BlackBerries shortly before takeoff. Or we visit the doctor or the car mechanic, generating digital records of what our medical or automotive problems are. We post blog entries confiding to the world our thoughts and feelings, or maintain personal social network profiles revealing our friendships and our tastes. Each of these transactions leaves digital breadcrumbs which, when pulled together, offer increasingly comprehensive pictures of both individuals and groups, with the potential of transforming our understanding of our lives, organizations, and societies in a fashion that was barely conceivable just a few years ago.
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@cite_21: Social networks evolve over time, driven by the shared activities and affiliations of their members, by similarity of individuals' attributes, and by the closure of short network cycles. We analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes. We found that network evolution is dominated by a combination of effects arising from network topology itself and the organizational structure in which the network is embedded. In the absence of global perturbations, average network properties appear to approach an equilibrium state, whereas individual properties are unstable.
@cite_3: This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labeled, streaming graph data. We introduce a generalization of the BTER model of by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into the graphs' structure and helps contextualized detected event. For evaluation, two new hierarchical detectors are tested against a baseline Gaussian method on a synthetic graph sequence with seeded anomalies. We demonstrate that in a labeled setting with community structure, our graph statistics-based approach outperforms both a distribution-based detector and the baseline, accurately detecting anomalies at the node, subgraph, and graph levels.
@cite_24: Social structure influences ecological processes such as dispersal and invasion, and affects survival and reproductive success. Recent studies have used static snapshots of social networks, thus neglecting their temporal dynamics, and focused primarily on a limited number of variables that might be affecting social structure. Here, instead we modelled effects of multiple predictors of social network dynamics in the spotted hyena, using observational data collected during 20 years of continuous field research in Kenya. We tested the hypothesis that the current state of the social network affects its long-term dynamics. We employed stochastic agent-based models that allowed us to estimate the contribution of multiple factors to network changes. After controlling for environmental and individual effects, we found that network density and individual centrality affected network dynamics, but that social bond transitivity consistently had the strongest effects. Our results emphasise the significance of structural properties of networks in shaping social dynamics.
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@cite_12: Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design Longitudinal social network analysis. Setting Framingham Heart Study social network. Participants 4739 individuals followed from 1983 to 2003. Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. ResultsClustersofhappyand unhappypeoplearevisible in the network, and the relationship between people ’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People whoaresurroundedbymanyhappypeopleandthosewho arecentralinthenetworkaremorelikelytobecomehappy in the future. Longitudinal statistical models suggest that clustersofhappinessresultfromthespreadofhappiness and not just a tendency for people to associate with similarindividuals.Afriendwholiveswithinamile(about 1.6km)andwhobecomeshappyincreasestheprobability that a person is happy by 25 (95 confidence interval 1 to 57 ). Similar effects are seen in coresident spouses (8 , 0.2 to 16 ), siblings who live within a mile (14 , 1 to 28 ), and next door neighbours (34 , 7 to 70 ). Effects are not seen between coworkers. The
It is widely accepted that structural properties of a network play a significant role in determining its actors' behavior @cite_41 @cite_25 @cite_38 @cite_34 @cite_21 @cite_27 . The last decade's abundance of temporal information paved the path to a further understanding of the dynamics of networks @cite_36 , and findings corroborate that structural properties have a prominent affect on the longitudinal dynamics of networks and their actors @cite_21 @cite_3 @cite_12 @cite_9 @cite_24 .