[Talk Summary] Anomaly Detection in Large Graphs
Professor Christos Faloutsos from Carnegie Mellon University held a talk on "Anomaly Detection in Large Graphs" on February, 24, 2017 at University of Pittsburgh. Given a large graph such as who-follows-whom, who-calls-whom, or who-likes-whom, observing some patterns in the graph can we tell what is normal behaviors and what is abnormal behaviors, which probably are resulted from fraudulent activities? And how graph evolves over time? Prof. Faloutsos presented two parts: (1) how to mine patterns and how to detect fraud in a static graph, and (2) patterns and anomalies in large time-evolving graphs. In the first part, Prof. Faloutsos claimed that real graphs are not random, for example, in- and out- degree distributions. He gave a list of laws and patterns of graphs, including: The power law in the degree distribution (connected component sizes): we can find patterns about the degree of graph. Singular values and eigen values: also used to find patterns about degree dist