Name
Title | ||
|---|---|---|
Analyzing the effectiveness of graph metrics for anomaly detection in online social networks |
Abstract | ||
|---|---|---|
Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users' graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-35063-4_45 | WISE |
Keywords | Field | DocType |
anomaly detection,various graph theoretic property,anomalous user,empirical analysis,online social network,community cohesiveness,average betweenness centrality,anomalous relationship,graph metrics,common rule,betweenness centrality | Network science,Anomaly detection,Data mining,Graph,Social network,Computer science,Common Rule,Group cohesiveness,Betweenness centrality,Alpha centrality | Conference |
Citations | PageRank | References |
10 | 0.53 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reza Hassanzadeh | 1 | 76 | 6.02 |
| Richi Nayak | 2 | 706 | 79.67 |
| Douglas Stebila | 3 | 578 | 48.66 |