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. Attached Files Name Description MIMEType Size Downloads Title Identifying diffusion sources in large networks: a community structure based approach Author(s) Jiang, Jiaojiao Zhou, Andi Majbouri Yazdi, Kasra Wen, Sheng Yu, Shui orcid.org/0000-0003-4485-6743 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 15 Aug 2016, 13:56:11 EST Mon, 15 Aug 2016, 16:16:59 EST Mon, 15 Aug 2016, 16:17:55 EST Mon, 15 Aug 2016, 16:18:53 EST Wed, 07 Sep 2016, 11:37:24 EST Wed, 12 Oct 2016, 15:45:47 EST Wed, 01 Mar 2017, 08:17:23 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 115 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 15 Aug 2016, 13:56:11 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.