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Real-World Applications of Big Data Analytics with Graph and Time Series Analysis by Christos Faloutsos

Dive into the realm of big data analytics as applied by Christos Faloutsos, exploring anomaly detection, time series analysis, and fraud detection. Learn from his research in network effects tools and cutting-edge technologies for real-world data mining applications.

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Real-World Applications of Big Data Analytics with Graph and Time Series Analysis by Christos Faloutsos

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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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