Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Scan Statistics on Enron Graphs
Computational & Mathematical Organization Theory
Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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People's email communications can be modeled as graphs with vertices representing email accounts and edges representing email communications. Email communication data usually comes in as continuous data stream. Event detection aims to identify abnormal email communications that serve as analogs of real-world events imposed upon the data stream. The goal is to understand the communications behaviors of the subjects. The contents of emails are often not available or protected by privacy, which makes linkage information the only resource we can rely on. We propose a link-based event detection method that clusters vertices with similar communication patterns together and then, considers deviations from each vertex's individual profile, as well as its cluster profile. Experiments show that this method performs well on both Enron and our own email datasets.