Mining Large Graphs: Patterns, Anomalies, and Fraud Detection

by Christos Faloutsos

UCSD, Distinguished Lecture Series

 Oct. 10, 2016

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, 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 discoveries such settings.

Foils

Foils in  pdf.


Last updated by: Christos Faloutsos, Oct. 9, 2016.