CATCHSYNC:
CATCHING SYNCHRONIZED
BEHAVIOR IN LARGE
DIRECTED GRAPHS
Meng Jiang, Tsinghua University, Beijing, China
Joint work with Peng Cui, Alex Beutel,
Christos Faloutsos and Shiqiang Yang
August 26, 2014 – NYC, USA
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Fraud Detection: Graph Analysis Problem
[www.buyfollowz.org]
[buymorelikes.com]
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Fraud Detection: Graph Analysis Problem
[buycheaplikes.com]
[reviewsteria.com]
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Our Goals
• Given: A graph (large-scale, directed, etc.)
• Find: Frauds = Anomalous edges
• Goals:
• G1. Find patterns that distinguish fraudsters
from normal users
• G2. Design algorithms that catch fraudsters
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OUTLINE
1. Background
2. Fraudulent Pattern
3. The Algorithm
4. Experiments
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Anomalies in Degree Distributions
• Power-law distribution
DBLP
Author-publication
Flickr
User-user
Twitter
Who-follows-whom
[konect.uni-koblenz.de/networks/]
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Anomalies in Degree Distributions
2009
3.17M
0.41M
41M
d=20
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Linear Classifier with “Degree”: Fail
=20?
+1
(Fraud)
Label
(+1,-1)
3.17M
0.41M
Out-degree
d=20
classifier
×
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Graph Structure Distorted
2011
1.91M
117M
0.44M
d=64
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Traditional Fraud Detection
Big? Small?
+1
(Fraud)
Label
(+1,-1)
Out-degree In-degree
classifier
Big?
#tweet
Big?
#url in
tweets
Big?
#hashtag
in tweets
Content-based
features
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Empty Profile?
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Few Followers?
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Many Followings?
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Content: Unavailable? Look Normal?
0, 0, 0… sorry
Label
(+1,-1)
Out-degree In-degree
classifier
#tweet
#url in
tweets
#hashtag
in tweets
Content-based
features
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Behavior is the Key
Monetary Incentive
Content
Behavior/
Links
what they
appear to
behave
what they
have to
behave
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OUTLINE
1. Background
2. Fraudulent Pattern
3. The Algorithm
4. Experiments
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Behavior-based Features
Follower
behavior
≈
Out-degree
1st left singular vector
(Hubness)
2nd left singular vector
…
Followee
behavior
≈
In-degree
1st right singular vector
(Authoritativeness)
2nd right singular vector
…
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Behavior-based Feature Space
Follower
behavior
Followee
behavior
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Fraudulent Behavior Patterns
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Fraudulent Behavior Patterns
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Fraudulent Behavior Patterns
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Fraudulent Behavior Patterns
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Fraudulent Behavior Patterns
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Fraudulent Behavior Patterns
• Synchronized
• Abnormal
25
OUTLINE
1. Background
2. Fraudulent Pattern
3. The Algorithm
4. Experiments
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Synchronicity and Normality
• Synchronicity
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Synchronicity and Normality
• Normality
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Synchronicity-Normality Plot
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Theorem
• For any distribution, there is a parabolic lower
limit in the synchronicity-normality plot.
synchronicity
• Proof. See our paper 
normality
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CatchSync Algorithm
• Distance-based
anomaly detection
• Fraudsters
• Big synchronicity
• Small normality
• Away from the densest
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OUTLINE
1. Background
2. Fraudulent Pattern Mining
3. The Algorithm
4. Experiments
32
Experiments
• Q1: Does CatchSync remove anomalies?
• Degree distribution
• Feature space
• Q2: Is CatchSync catching actually
fraudulent users?
• Q3: Is CatchSync robust?
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Q1: Does CatchSync Remove Anomalies?
2009
3.17M
41M
0.41M
d=20
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Q1: Does CatchSync Remove Anomalies?
2011
117M
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Before CatchSync
Follower
behavior
Followee
behavior
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After CatchSync
Follower
behavior
Followee
behavior
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Q2: Is CatchSync Catching Actually
Fraudulent Users?
173/1,000
237/1,000
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Q2: Is CatchSync Catching Actually
Fraudulent Users?
CatchSync
+SPOT
0.813
CatchSync
0.751
0.597
SPOT
OutRank
0.412
0
0.2
0.4
0.6
0.8
1
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Q2: Is CatchSync Catching Actually
Fraudulent Users?
CatchSync
+SPOT
0.785
CatchSync
0.694
0.653
SPOT
OutRank
0.377
0
0.2
0.4
0.6
0.8
1
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Q2: Is CatchSync Catching Actually
Fraudulent Users?
Recall = 80%
Precision in Twitter
Precision in Tencent Weibo
83.5%
79.4%
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Q3: Is CatchSync Robust to Camouflage?
Target
Popular
camouflage
Random
camouflage
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Q3: Is CatchSync Robust to Camouflage?
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Q3: Is CatchSync Robust to Camouflage?
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Q3: Is CatchSync Robust to Camouflage?
Popular
camouflage
Random
camouflage
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Conclusion
• Goals
• G1. Find patterns that distinguish fraudulent
user behavior from normal behavior
• A1: Synchronized & Abnormal!
• G2. Design algorithms that catch fraudsters
• A2: CatchSync!
• Remove spikes
• Content free
• Robust to camouflage
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Questions?
Meng Jiang
mjiang89@gmail.com
http://www.meng-jiang.com