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DAPA-V10: Discovery and Analysis of Patterns and Anomalies in Volatile Time-Evolving Networks

We address the problems of finding patterns and detecting anomalous activities in volatile time-evolving networks such as communication networks (as opposed to slowly evolving networks like co-authorship graphs). Our approach, DAPA-V10, utilizes a simple compact graph representation that assigns weights to edges in a way that captures the frequency, duration, and recency of edges. Given this weighted “cumulative” graph, DAPA-V10 finds persistent patterns by extracting connected components of regularly-occurring edges. These persistent patterns provide a basis for expected normal behavior in the network over time, which are then utilized to detect anomalous behavior on both local and global scales. In particular, DAPA-V10 uses a scalable approach based on the Product Rule for the Central Limit Theorem to measure the likelihood of events and flag anomalies. Figure 1 provides an overview of DAPA-V10. Experiments on the Enron email dataset illustrate the effectiveness of our approach.