Mining Large Graphs: Patterns, Anomalies and Fraud Detection
Distinguished guest lecture - York University
February 5, 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, 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, how to handle large tensors in map-reduce environments, as well as some discoveries such settings.
We conclude with some open research questions for graph mining.
Foils
Foils in pdf
are here.
Last updated by: Christos Faloutsos, Feb. 4, 2016.