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Identifying diffusion sources in large networks: a community structure based approach

Jiang, Jiaojiao, Zhou, Andi, Majbouri Yazdi, Kasra, Wen, Sheng, Yu, Shui and Xiang, Yang 2015, Identifying diffusion sources in large networks: a community structure based approach, in TrustCom 2015: Proceedings of the IEEE Trust, Security and Privacy in Computing and Communications 2015 International Conference, IEEE, Piscataway, N.J., pp. 302-309, doi: 10.1109/Trustcom.2015.388.

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Title Identifying diffusion sources in large networks: a community structure based approach
Author(s) Jiang, Jiaojiao
Zhou, Andi
Majbouri Yazdi, Kasra
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
Conference name IEEE Trust, Security and Privacy in Computing and Communications. International Conference (14th : 2015 : Helsinki, Finland)
Conference location Helsinki, Finland
Conference dates 2015/08/20 - 2015/08/22
Title of proceedings TrustCom 2015: Proceedings of the IEEE Trust, Security and Privacy in Computing and Communications 2015 International Conference
Editor(s) [Unknown],
Publication date 2015
Start page 302
End page 309
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) source identification
scalability
sensor techniques
community structure
Summary The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, most methods proposed so far are unsuitable for large networks because of their computational cost and the complex spatiotemporal diffusion processes. In this paper, we develop a community structure based approach to efficiently identify diffusion sources in large networks. We first detect the community structure of a network and assign sensors on community bridge nodes to record diffusion dynamics. From the infection time of bridge sensors, we can determine the very first infected community from which the diffusion started and spread out to other communities. This, therefore, overcomes the scalability issue in source identification problems by narrowing the set of suspects down to the first infected community. Then, to accurately locate the diffusion source from suspects, we utilize an intrinsic feature of diffusion sources that the relative infection time of any node is linear with its effective distance from the diffusion source. Thus, for each suspect, we compute the correlation coefficient to measure the degree of linear dependence between sensors' relative infection times and their effective distances from the suspect, and consider the one with the greatest correlation coefficient as the source. We evaluate our approach in two large networks containing more than 300,000 nodes, which are collected from Twitter. The experiment results show that our method can identify diffusion sources with very high degree of accuracy. Especially when the average community size shrinks, the accuracy of our approach increases dramatically.
ISBN 9781467379519
Language eng
DOI 10.1109/Trustcom.2015.388
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085392

Document type: Conference Paper
Collection: School of Information Technology
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