TRIAGE: Temporal Twitter attribute graph patterns

Given a node-attributed network of Twitter users, can we capture their posting behavior over time and identify patterns that could probably describe, model or predict their activity? Based on the assumption that the posts of these users are topic-specific, can we identify temporal connectivity patterns that emerge from the use of specific attributes? More challengingly, are there any particular attribute usage patterns which indicate an inherent anomaly either for users or attributes? Our study attempts to provide solid answers to all the above questions, extending previous work on other social networks and attribute types. We propose TRIAGE, a pipeline of methods which: (a) identify temporal behavioral patterns in individual attribute distributions, (b) model the temporal evolution of attribute induced graphs and (c) detect irregular attributes and users based on the patterns identified earlier; More specifically, we model the attribute distributions using the log-Odds ratio, we provide explanations with respect to the attribute induced subgraph patterns and we observe the structural differences of attribute induced subgraphs based on these patterns. Experimental results show that: most of the individual attribute distributions remain stable over time following mostly power laws norm; the temporal evolution of attribute induced graphs obey certain laws and deviations are outliers; finally, we discover that we can indeed identify the structure of each subgraph, based on the emerging patterns. Real dataset experiments on 50K Twitter users activities and attributes has successfully proven that TRIAGE has effectively identified Twitter user and attribute behavioral patterns and can identify irregular activities for users and anomalous graph structures for attribute induced subgraphs.