Mining Large Graphs PDF
Preview Mining Large Graphs
CMU SCS Mining Large Graphs: Patterns, Anomalies, and Fraud Detection Christos Faloutsos CMU CMU SCS Thank you! • Nina Balcan • Kilian Weinberger ICML'16 (c) 2016, C. Faloutsos 2 CMU SCS Roadmap • Introduction – Motivation – Why study (big) graphs? • Part#1: Patterns in graphs • Part#2: time-evolving graphs; tensors • Conclusions ICML'16 (c) 2016, C. Faloutsos 3 CMU SCS Graphs - why should we care? ~1B nodes (web sites) ~6B edges (http links) ‘YahooWeb graph’ ICML'16 (c) 2016, C. Faloutsos 4 CMU SCS Graphs - why should we care? >$10B; ~1B users ICML'16 (c) 2016, C. Faloutsos 5 CMU SCS Graphs - why should we care? Internet Map Food Web [lumeta.com] [Martinez ’91] ICML'16 (c) 2016, C. Faloutsos 6 CMU SCS Graphs - why should we care? • web-log (‘blog’) news propagation • computer network security: email/IP traffic and anomaly detection • Recommendation systems • .... • Many-to-many db relationship -> graph ICML'16 (c) 2016, C. Faloutsos 7 CMU SCS Motivating problems • P1: patterns? Fraud detection? • P2: patterns in time-evolving graphs / tensors destination time ICML'16 (c) 2016, C. Faloutsos 8 CMU SCS Motivating problems • P1: patterns? Fraud detection? Patterns anomalies • P2: patterns in time-evolving graphs / tensors destination time ICML'16 (c) 2016, C. Faloutsos 9 CMU SCS Motivating problems • P1: patterns? Fraud detection? Patterns anomalies* • P2: patterns in time-evolving graphs / tensors destination time ICML'16 (c) 2016, C. Faloutsos 10 * Robust Random Cut Forest Based Anomaly Detection on Streams Sudipto Guha, Nina Mishra , Gourav Roy, Okke Schrijvers, ICML’16
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