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Mining Large Graphs PDF

113 Pages·2016·9.31 MB·English
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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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Patterns, Anomalies, and Fraud. Detection. Christos Faloutsos. CMU Weinberger. ICML'16. (c) 2016, C. Faloutsos. 2 .. Charu Aggarwal) . Retweeting: “Galaxy Note Dream Project: .. Large datasets reveal patterns/outliers that.
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