Presentation is loading. Please wait.

Presentation is loading. Please wait.

CMU SCS Big (graph) data analytics Christos Faloutsos CMU.

Published byDylan Marshall Modified over 9 years ago

Similar presentations

Presentation on theme: "CMU SCS Big (graph) data analytics Christos Faloutsos CMU."— Presentation transcript:

1 CMU SCS Big (graph) data analytics Christos Faloutsos CMU

2 CMU SCS CMU visit '14C. Faloutsos2 Outline Problem definition / Motivation Anomaly detection Time series analysis Conclusions

3 CMU SCS CMU visit '14C. Faloutsos3 Motivation Data mining: ~ find patterns (rules, outliers) How do real graphs look like? Anomalies? Time series / Monitoring Measles @ PA, NY, …

4 CMU SCS CMU visit '14C. Faloutsos4 Graphs - why should we care?

5 CMU SCS C. Faloutsos5 Graphs - why should we care? Internet Map [lumeta.com] Food Web [Martinez ’91] ~1B users $10-$100B revenue CMU visit '14

6 CMU SCS CMU visit '14C. Faloutsos6 Outline Problem definition / Motivation Anomaly/fraud detection –Financial fraud –Ebay fraud Time Series Analysis Conclusions

7 CMU SCS Network Effect Tools: SNARE 7 Some accounts are sort-of-suspicious – how to combine weak signals? Before CMU visit '14C. Faloutsos

8 CMU SCS Network Effect Tools: SNARE 8 A: Belief Propagation. Before CMU visit '14C. Faloutsos

9 CMU SCS Network Effect Tools: SNARE 9 A: Belief Propagation. After Before CMU visit '14C. Faloutsos 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

10 CMU SCS Network Effect Tools: SNARE 10 Produces improvement over simply using flags –Up to 6.5 lift –Improvement especially for low false positive rate False positive rate True positive rate Results for accounts data (ROC Curve) Ideal SNARE Baseline (flags only) CMU visit '14C. Faloutsos

11 CMU SCS Network Effect Tools: SNARE 11 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 CMU visit '14C. Faloutsos

12 CMU SCS CMU visit '14C. Faloutsos12 Outline Problem definition / Motivation Anomaly/fraud detection –Financial fraud –Ebay fraud Time series analysis Conclusions

13 CMU SCS 3 - 13C. Faloutsos E-bay Fraud detection Detects ‘non-delivery’ fraud: seller takes $$ and disappears CMU visit '14 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.

14 CMU SCS 3 - 14C. Faloutsos E-bay Fraud detection - NetProbe CMU visit '14

15 CMU SCS App-store fraud Opinion Fraud Detection in Online Reviews using Network Effects Leman Akoglu, Rishi Chandy, CF ICWSM’13 CMU visit '14C. Faloutsos15

16 CMU SCS Problem Given –user-product review network –review sign (+/-) Classify –objects into type-specific classes: users: `honest’ / `fraudster’ products: `good’ / `bad’ reviews: `genuine’ / `fake’ No side data! (e.g., timestamp, review text) CMU visit '14C. Faloutsos16

17 CMU SCS Formulation: BP UserProduct honestbad honestgood CMU visit '14C. Faloutsos17 – + Before After

18 CMU SCS Top scorers CMU visit '14C. Faloutsos18 + positive (4-5) rating o negative (1-2) rating Users Products

19 CMU SCS Top scorers CMU visit '14C. Faloutsos19 + positive (4-5) rating o negative (1-2) rating Users Products

20 CMU SCS ‘Fraud-bot’ member reviews CMU visit '14C. Faloutsos20 Same developer!Duplicated text! Same day activity!

21 CMU SCS CMU visit '14C. Faloutsos21 Outline Problem definition / Motivation Anomaly/fraud detection Time series, monitoring / forecasting Conclusions

22 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1422C. Faloutsos Yasuko Matsubara 50 states x 46 diseases

23 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1423C. Faloutsos Prof. Yasuko Matsubara

24 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1424C. Faloutsos Prof. Yasuko Matsubara Flu? Measles? August? No periodicity?

25 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1425C. Faloutsos Prof. Yasuko Matsubara Flu? Measles? August? No periodicity?

26 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1426C. Faloutsos Prof. Yasuko Matsubara Flu? Measles? August? No periodicity?

27 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1427C. Faloutsos Prof. Yasuko Matsubara Flu? Measles? August? No periodicity?

28 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1428C. Faloutsos Prof. Yasuko Matsubara Flu? Measles? August? No periodicity?

29 CMU SCS ‘Tycho’ – epidemics analysis CMU visit '1429C. Faloutsos 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.

30 CMU SCS 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’) CMU visit '14C. Faloutsos30

31 CMU SCS CMU visit '14C. Faloutsos31 Contact info www.cs.cmu.edu/~christos GHC 8019 Ph#: x8.1457

Download ppt "CMU SCS Big (graph) data analytics Christos Faloutsos CMU."

Similar presentations

CMU SCS Identifying on-line Fraudsters: Anomaly Detection Using Network Effects Christos Faloutsos CMU.

CMU SCS Identifying on-line Fraudsters: Anomaly Detection Using Network Effects Christos Faloutsos CMU.
CMU SCS I2.2 Large Scale Information Network Processing INARC 1 Overview Goal: scalable algorithms to find patterns and anomalies on graphs 1. Mining Large.

CMU SCS I2.2 Large Scale Information Network Processing INARC 1 Overview Goal: scalable algorithms to find patterns and anomalies on graphs 1. Mining Large.
FUNNEL: Automatic Mining of Spatially Coevolving Epidemics Yasuko Matsubara, Yasushi Sakurai (Kumamoto University) Willem G. van Panhuis (University of.

FUNNEL: Automatic Mining of Spatially Coevolving Epidemics Yasuko Matsubara, Yasushi Sakurai (Kumamoto University) Willem G. van Panhuis (University of.
School of Computer Science Carnegie Mellon University Duke University DeltaCon: A Principled Massive- Graph Similarity Function Danai Koutra Joshua T.

School of Computer Science Carnegie Mellon University Duke University DeltaCon: A Principled Massive- Graph Similarity Function Danai Koutra Joshua T.
School of Computer Science Carnegie Mellon University National Taiwan University of Science & Technology Unifying Guilt-by-Association Approaches: Theorems.

School of Computer Science Carnegie Mellon University National Taiwan University of Science & Technology Unifying Guilt-by-Association Approaches: Theorems.
1 CANTINA : A Content-Based Approach to Detecting Phishing Web Sites WWW 2007 2008.09.09 Yue Zhang, Jason Hong, and Lorrie Cranor.

1 CANTINA : A Content-Based Approach to Detecting Phishing Web Sites WWW Yue Zhang, Jason Hong, and Lorrie Cranor.
Node labels as random variables prior belief observed neighbor potentials compatibility potentials Opinion Fraud Detection in Online Reviews using Network.

Node labels as random variables prior belief observed neighbor potentials compatibility potentials Opinion Fraud Detection in Online Reviews using Network.
CMU SCS 15-826: Multimedia Databases and Data Mining Lecture#1: Introduction Christos Faloutsos CMU www.cs.cmu.edu/~christos.

CMU SCS : Multimedia Databases and Data Mining Lecture#1: Introduction Christos Faloutsos CMU
CMU SCS Large Graph Mining - Patterns, Tools and Cascade Analysis Christos Faloutsos CMU.

CMU SCS Large Graph Mining - Patterns, Tools and Cascade Analysis Christos Faloutsos CMU.
Automated Anomaly Detection, Data Validation and Correction for Environmental Sensors using Statistical Machine Learning Techniques www.aquaticinformatics.com.

Automated Anomaly Detection, Data Validation and Correction for Environmental Sensors using Statistical Machine Learning Techniques
SFU, CMPT 741, Fall 2009, Martin Ester 418 Outlook Outline Trends in KDD research Graph mining and social network analysis Recommender systems Information.

SFU, CMPT 741, Fall 2009, Martin Ester 418 Outlook Outline Trends in KDD research Graph mining and social network analysis Recommender systems Information.
CMU SCS C. Faloutsos (CMU)#1 Large Graph Algorithms Christos Faloutsos CMU McGlohon, Mary Prakash, Aditya Tong, Hanghang Tsourakakis, Babis Akoglu, Leman.

CMU SCS C. Faloutsos (CMU)#1 Large Graph Algorithms Christos Faloutsos CMU McGlohon, Mary Prakash, Aditya Tong, Hanghang Tsourakakis, Babis Akoglu, Leman.
NetMine: Mining Tools for Large Graphs Deepayan Chakrabarti Yiping Zhan Daniel Blandford Christos Faloutsos Guy Blelloch.

NetMine: Mining Tools for Large Graphs Deepayan Chakrabarti Yiping Zhan Daniel Blandford Christos Faloutsos Guy Blelloch.
CMU SCS Mining Billion-node Graphs Christos Faloutsos CMU.

CMU SCS Mining Billion-node Graphs Christos Faloutsos CMU.
Shipi Kankane Prashanth Nakirekommula.  Applying analytics and risk- management capabilities to health insurance through LexisNexis data platforms. 

Shipi Kankane Prashanth Nakirekommula.  Applying analytics and risk- management capabilities to health insurance through LexisNexis data platforms. 
Social Networks and Graph Mining Christos Faloutsos CMU - MLD.

Social Networks and Graph Mining Christos Faloutsos CMU - MLD.
WindMine: Fast and Effective Mining of Web-click Sequences SDM 2011Y. Sakurai et al.1 Yasushi Sakurai (NTT) Lei Li (Carnegie Mellon Univ.) Yasuko Matsubara.

WindMine: Fast and Effective Mining of Web-click Sequences SDM 2011Y. Sakurai et al.1 Yasushi Sakurai (NTT) Lei Li (Carnegie Mellon Univ.) Yasuko Matsubara.
Neighborhood Formation and Anomaly Detection in Bipartite Graphs Jimeng Sun Huiming Qu Deepayan Chakrabarti Christos Faloutsos Speaker: Jimeng Sun.

Neighborhood Formation and Anomaly Detection in Bipartite Graphs Jimeng Sun Huiming Qu Deepayan Chakrabarti Christos Faloutsos Speaker: Jimeng Sun.
CMU SCS Large Graph Mining Christos Faloutsos CMU.

CMU SCS Large Graph Mining Christos Faloutsos CMU.
Detecting Fraudulent Personalities in Networks of Online Auctioneers Duen Horng (“Polo”) Chau Shashank Pandit Christos Faloutsos School of Computer Science.

Detecting Fraudulent Personalities in Networks of Online Auctioneers Duen Horng (“Polo”) Chau Shashank Pandit Christos Faloutsos School of Computer Science.

Similar presentations

Ads by Google