Anomaly Detection in Large Graphs

Christos Faloutsos, CMU

Lecture Series
Samsung AI, Cambridge
May 18, 2020.

Abstract

Given a large graph, like who-calls-whom, or who-likes-whom, what behavior is normal and what should be surprising, possibly due to fraudulent activity? How do graphs evolve over time? We focus on these topics: (a) anomaly detection in large static graphs and (b) patterns and anomalies in large time-evolving graphs.

For the first, we present a list of static and temporal laws, including advances patterns like 'eigenspokes'; we show how to use them to spot suspicious activities, in on-line buyer-and-seller settings, in FaceBook, in twitter-like networks. For the second, we show how to handle time-evolving graphs as tensors, as well as some surprising discoveries such settings.

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Last updated by: Christos Faloutsos, May 17, 2020.