Name
Abstract | ||
|---|---|---|
Online Social Networks as new phenomenon have affected our life in many positive ways; however it can be considered as way of malicious activities. Identifying anomalous users has become a challenge and many researches are conducted but they are not enough and in this paper we propose a methodology based on graph metrics of online social networks. The experimental results illustrate that majority of friends in online social networks have common friends with their friends while anomalous users may not follow this fact. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICITST.2013.6750277 | Internet Technology and Secured Transactions |
Keywords | Field | DocType |
graph theory,security of data,social networking (online),anomalous users identification,anomaly detection,graph metrics,online social networks,structure-based technique,anomaly detection,graph mining,online social networks | Graph,Anomaly detection,Social network,Computer science,Computer security,Robustness (computer science),Artificial intelligence,Atmospheric measurements,Phenomenon,Programming profession,Machine learning | Conference |
ISSN | Citations | PageRank |
2164-7046 | 4 | 0.40 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdolazim Rezaei | 1 | 4 | 0.40 |
| Zarinah Mohd Kasirun | 2 | 28 | 3.89 |
| Vala Ali Rohani | 3 | 63 | 4.07 |
| Touraj Khodadadi | 4 | 32 | 2.10 |