Speaker: Christos Faloutsos
Professor, Computer Science
Carnegie Mellon University
Title: Mining Large Graphs
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? How does influence/news/viruses propagate, over time? We focus on three topics: (a) anomaly detection in large static graphs (b) patterns and anomalies in large time-evolving graphs and (c) cascades and immunization.
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.
For the third, we show that for virus propagation, a single number is enough to characterize the connectivity of graph, and thus we show how to do efficient immunization for almost any type of virus (SIS - no immunity; SIR - lifetime immunity; etc)
We conclude with some open research questions for graph mining.
Christos Faloutsos received the Presidential Young Investigator Award by the National Science Foundation in 1989, the Research Contributions Award in ICDM in 2006, the SIGKDD Innovations Award in 2010, and two Test of Time awards. He is an ACM Fellow, and has served as a member of the executive committee of SIGKDD. He holds eight patents and he has given over 35 tutorials and over 15 invited distinguished lectures. His research interests include data mining for graphs and streams, fractals, database performance, and indexing for multimedia and bio-informatics data.
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