Rumor source detection in finite graphs with boundary effects by message-passing algorithms

Pei Duo Yu, Chee Wei Tan, Hung-Lin Fu

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

Finding information source in viral spreading has important applications such as to root out the culprit of a rumor spreading in online social networks. In particular, given a snapshot observation of the rumor graph, how to accurately identify the initial source of the spreading? In the seminal work by Shah and Zaman in 2011, this statistical inference problem was formulated as a maximum likelihood estimation problem and solved using a rumor centrality approach for graphs that are degree-regular. This however is optimal only if there are no boundary effects, e.g., the underlying number of susceptible vertices is countably infinite. In general, all practical real world networks are finite or exhibit complex spreading behavior, and therefore these boundary effects cannot be ignored. In this paper, we solve the constrained maximum likelihood estimation problem by a generalized rumor centrality for spreading in graphs with boundary effects. We derive a graph-theoretic characterization of the maximum likelihood estimator for degree-regular graphs with a single end vertex at its boundary and propose a message-passing algorithm that is near-optimal for graphs with more complex boundary consisting of multiple end vertices.

原文English
主出版物標題Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
編輯Jana Diesner, Elena Ferrari, Guandong Xu
發行者Association for Computing Machinery, Inc
頁面86-90
頁數5
ISBN(電子)9781450349932
DOIs
出版狀態Published - 31 七月 2017
事件9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
持續時間: 31 七月 20173 八月 2017

出版系列

名字Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Conference

Conference9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
國家Australia
城市Sydney
期間31/07/173/08/17

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  • 引用此

    Yu, P. D., Tan, C. W., & Fu, H-L. (2017). Rumor source detection in finite graphs with boundary effects by message-passing algorithms. 於 J. Diesner, E. Ferrari, & G. Xu (編輯), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 (頁 86-90). (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3110025.3110028