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Towards anomalous diffusion sources detection in a large network

journal contribution
posted on 2016-01-01, 00:00 authored by P Zhang, J He, G Long, Guangyan HuangGuangyan Huang, C Zhang
Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.

History

Journal

ACM Transactions on Internet Technology

Volume

16

Article number

2

Pagination

1-24

Location

New York, N.Y.

ISSN

1533-5399

eISSN

1557-6051

Language

English

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2016, ACM Digital Library

Issue

1

Publisher

Association for Computing Machinery