Anomaly Detection in Large Graphs
University of Pittsburgh
February 24, 2017
Given a large graph, like who-calls-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
(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.
Last updated by: Christos Faloutsos, Feb. 2017.