Role-dynamics: fast mining of large dynamic networks
Proceedings of the 21st international conference companion on World Wide Web
SigSpot: mining significant anomalous regions from time-evolving networks (abstract only)
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Modeling dynamic behavior in large evolving graphs
Proceedings of the sixth ACM international conference on Web search and data mining
Visual analysis of large-scale network anomalies
IBM Journal of Research and Development
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We address the problem of detecting characteristic patterns in communication networks. We introduce a scalable approach based on set-system discrepancy. By implicitly labeling each network edge with the sequence of times in which its two endpoints communicate, we view an entire communication network as a set-system. This view allows us to use combinatorial discrepancy as a mechanism to "observe" system behavior at different time scales. We illustrate our approach, called Discrepancy-based Novelty Detector (DND), on networks obtained from emails, blue tooth connections, IP traffic, and tweets. DND has almost linear runtime complexity and linear storage complexity in the number of communications. Examples of novel discrepancies that it detects are (i) asynchronous communications and (ii) disagreements in the firing rates of nodes and edges relative to the communication network as a whole.