K-Center: an approach on the multi-source identification of information diffusion

Jiang, Jiaojiao, Wen, Sheng, Yu, Shui, Xiang, Yang and Zhou, Wanlei 2015, K-Center: an approach on the multi-source identification of information diffusion, IEEE transactions on information forensics and security, vol. 10, no. 12, pp. 2616-2626, doi: 10.1109/TIFS.2015.2469256.

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Title K-Center: an approach on the multi-source identification of information diffusion
Author(s) Jiang, Jiaojiao
Wen, Sheng
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Journal name IEEE transactions on information forensics and security
Volume number 10
Issue number 12
Start page 2616
End page 2626
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-12-01
ISSN 1556-6013
Summary The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is O(mn log α) , where α = α (m,n) is the slowly growing inverse-Ackermann function, n is the number of infected nodes, and m is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.
Language eng
DOI 10.1109/TIFS.2015.2469256
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
09 Engineering
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082073

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