Publication Type

Conference Proceeding Article

Publication Date

12-2012

Abstract

Bipartite graphs can model many real life applications including users-rating-products in online marketplaces, users-clicking-webpages on the World Wide Web and users referring users in social networks. In these graphs, the anomalousness of nodes in one partite often depends on that of their connected nodes in the other partite. Previous studies have shown that this dependency can be positive (the anomalousness of a node in one partite increases or decreases along with that of its connected nodes in the other partite) or negative (the anomalousness of a node in one partite rises or falls in opposite direction to that of its connected nodes in the other partite). In this paper, we unify both positive and negative mutual dependency relationships in an unsupervised framework for detecting anomalous nodes in bipartite graphs. This is the first work that integrates both mutual dependency principles to model the complete set of anomalous behaviors of nodes that cannot be identified by either principle alone. We formulate our principles and design an iterative algorithm to simultaneously compute the anomaly scores of nodes in both partites. Moreover, we mathematically prove that the ranking of nodes by anomaly scores in each partite converges. Our framework is examined on synthetic graphs and the results show that our model outperforms existing models with only positive or negative mutual dependency principles. We also apply our framework to two real life datasets: Goodreads as a users-rating-books setting and Buzzcity as a users-clickingadvertisements setting. The results show that our method is able to detect suspected spamming users and spammed books in Goodreads and achieve higher precision in identifying fraudulent advertisement publishers than existing approaches.

Keywords

Anomaly Detection, Bipartite Graph, Mutual Dependency, Mutual Reinforcement, Node Anomalies

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

2012 IEEE 12th International Conference on Data Mining: ICDM 2012: 10-13 December 2012, Brussels, Belgium

First Page

171

Last Page

180

ISBN

9781467346498

Identifier

10.1109/ICDM.2012.167

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.ieeecomputersociety.org/10.1109/ICDM.2012.167

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