Anomaly detection in large graphs
Christos Faloutsos, CMU
PNC workshop
June 15, 2023
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.
Bio
Christos Faloutsos is a Professor at Carnegie Mellon University. He is an ACM Fellow; he has published over 500 refereed articles, 17 book chapters and three monographs. He has received the SIGKDD Innovations Award (2010), and 31 ``best paper'' awards (including 8 ``test of time'' awards). His research interests include large-scale data mining with emphasis on graphs and time sequences; anomaly detection, tensors, and fractals.
Material
Last updated: June 6, 2023, by Christos Faloutsos