Finding a Needle in a Haystack: Detecting Outliers in Complex Networks
Dima Kagan, Michael Fire, Yuval Elovici
Related Works
- Reputation based filtering [Golbeck and Hendler].
- Topoplogy based identification [Fire et al.].
- Graph centrality measure based spammer identification [DeBarr and Wechsler].
- Spammers detection in social networks by using “honey-profiles" [Stringhini et al.].
- Clustering groups of accounts that act similarly at around the same time for a sustained period of time [Cao et al.].
Labeling Data is Hard
Link Prediction
+
Crowd Wisdom
Malicious Users Tend to Connect to Other Profiles Randomly
Topology Based
Feature Extraction
16 feautres
for directed
graphs
8 feautres for
undirected
graphs
◦ For undirected graphs:
- Common Friends
- Total Friends
- Jaccard’s-Coefficent
\frac{|\Gamma(v) \cap \Gamma(u)|}{|\Gamma(v) \cup \Gamma(u)|}
|\Gamma(v) \cup \Gamma(u)|
|\Gamma(v) \cap \Gamma(u)|
|\Gamma(v)_{in}| \cap |\Gamma_{out}(u)|
\begin{cases}
1, & \text{if}\ (u,v)\in E \\
0, & \text{otherwise}
\end{cases}
◦ For directed graphs:
- Transitive Friends
- Opposite Direction Friends
Link Classification
Aggregation of The Results
\sum_{}
Meta Feature Exteraction
AbnormalityVertexProbability(v) := \frac{1}{|\Gamma(v)|}\sum\nolimits_{u \in \Gamma(v)}p(v,u)
We extracted 9 features
- - the confidence that an edge is fake.
p(v,u)
Datasets
Real World Networks
Kids Friendship Network
AUC - 0.93
TPR - 0.91
FPR- 0.15
https://github.com/Kagandi/anomalous-vertices-detection
Questions?
Finding a Needle in a Haystack: Detecting Outliers in Complex Networks
By Dima Kagan
