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Big (graph) data analytics

Big (graph) data analytics. Christos Faloutsos CMU. Outline. Problem definition / Motivation Anomaly detection Time series analysis Conclusions. Motivation. Data mining: ~ find patterns (rules, outliers) How do real graphs look like? Anomalies? Time series / Monitoring.

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Big (graph) data analytics

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  1. Big (graph) data analytics Christos Faloutsos CMU

  2. Outline • Problem definition / Motivation • Anomaly detection • Time series analysis • Conclusions C. Faloutsos

  3. Motivation • Data mining: ~ find patterns (rules, outliers) • How do real graphs look like? Anomalies? • Time series / Monitoring Measles @ PA, NY, … C. Faloutsos

  4. Graphs - why should we care? C. Faloutsos

  5. Graphs - why should we care? Food Web [Martinez ’91] ~1B users $10-$100B revenue Internet Map [lumeta.com] C. Faloutsos

  6. Outline • Problem definition / Motivation • Anomaly/fraud detection • Financial fraud • Ebay fraud • Time Series Analysis • Conclusions C. Faloutsos

  7. Network Effect Tools: SNARE • Some accounts are sort-of-suspicious – how to combine weak signals? Before C. Faloutsos

  8. Network Effect Tools: SNARE • A: Belief Propagation. Before C. Faloutsos

  9. Network Effect Tools: SNARE • A: Belief Propagation. Before After Mary McGlohon, Stephen Bay, Markus G. Anderle, David M. Steier, Christos Faloutsos: SNARE: a link analytic system for graph labeling and risk detection. KDD 2009: 1265-1274 C. Faloutsos

  10. Network Effect Tools: SNARE • Produces improvement over simply using flags • Up to 6.5 lift • Improvement especially for low false positive rate Results for accounts data (ROC Curve) Ideal SNARE Baseline (flags only) True positive rate C. Faloutsos False positive rate

  11. Network Effect Tools: SNARE • Accurate- Produces large improvement over simply using flags • Flexible- Can be applied to other domains • Scalable- One iteration BP runs in linear time (# edges) • Robust- Works on large range of parameters C. Faloutsos

  12. Outline • Problem definition / Motivation • Anomaly/fraud detection • Financial fraud • Ebay fraud • Time series analysis • Conclusions C. Faloutsos

  13. E-bay Fraud detection Detects ‘non-delivery’ fraud: seller takes $$ and disappears Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos. NetProbe: A Fast and Scalable System for Fraud Detection in Online Auction Networks WWW 07. C. Faloutsos

  14. E-bay Fraud detection - NetProbe C. Faloutsos

  15. ‘Tycho’ – epidemics analysis Yasuko Matsubara 50 states x 46 diseases C. Faloutsos

  16. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara C. Faloutsos

  17. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara Flu? Measles? August? No periodicity? C. Faloutsos

  18. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara Flu? Measles? August? No periodicity? C. Faloutsos

  19. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara Flu? Measles? August? No periodicity? C. Faloutsos

  20. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara Flu? Measles? August? No periodicity? C. Faloutsos

  21. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara Flu? Measles? August? No periodicity? C. Faloutsos

  22. ‘Tycho’ – epidemics analysis Prof. Yasuko Matsubara https://www.tycho.pitt.edu/resources.php from U. Pitt (epidemiology dept.) Yasuko Matsubara, Yasushi Sakurai, Willem van Panhuis, and Christos Faloutsos, FUNNEL: Automatic Mining of Spatially Coevolving Epidemics, KDD 2014, New York City, NY, USA, Aug. 24-27, 2014. C. Faloutsos

  23. Open research questions • Patterns/anomalies for time-evolving graphs (Call graph, 3M people x 6mo) • Spot fraudsters in soc-net (eg., Twitter ‘$10 -> 1000 followers’) C. Faloutsos

  24. Contact info • www.cs.cmu.edu/~christos • GHC 8019 • Ph#: x8.1457 C. Faloutsos

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