MultiAspectForensics: mining large heterogeneous networks using tensor Online publication date: Sat, 16-Aug-2014
by Koji Maruhashi; Fan Guo; Christos Faloutsos
International Journal of Web Engineering and Technology (IJWET), Vol. 7, No. 4, 2012
Abstract: Modern applications such as web knowledge bases, network traffic monitoring and online social networks involve an unprecedented amount of 'heterogeneous' network data, with rich types of interactions among nodes. How can we find patterns and anomalies for heterogeneous networks with millions of edges that have high dimensional attributes, in a scalable way? We introduce MultiAspectForensics, a novel tool to automatically detect and visualise bursts of specific sub-graph patterns within a local community of nodes as anomalies in a heterogeneous network, leveraging scalable tensor analysis methods. One such pattern consists of a set of vertices that form a dense bipartite graph, whose edges share exactly the same set of attributes. We present empirical results of the proposed method on three datasets from distinct application domains, and discuss insights derived from these patterns discovered. Moreover, we empirically show that our algorithm can be feasibly applied to higher dimensional datasets.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Web Engineering and Technology (IJWET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com