University of Oulu

R. Jin, P. Garg, W. Wu, Q. Ni and R. E. Guadagno, "A Greedy Monitoring Station Selection for Rumor Source Detection in Online Social Networks," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2023.3284909

A greedy monitoring station selection for rumor source detection in online social networks

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Author: Jin, Rong1; Garg, Priyanshi2; Wu, Weili2;
Organizations: 1Department of Computer Science, College of Engineering and Computer Science, California State University, Fullerton, CA 92831 USA
2Department of Computer Science, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75080 USA
3School of Computers, Guangdong University of Technology, Guangzhou 510006, China
4Department of Persuasive Information Systems, University of Oulu, 90570 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-13


In monitoring station observation, for the best accuracy of rumor source detection, it is important to deploy monitors appropriately into the network. There are, however, a very limited number of studies on the monitoring station selection. This article will study the problem of detecting a single rumormonger based on an observation of selected infection monitoring stations in a complete snapshot taken at some time in an online social network (OSN) following the independent cascade (IC) model. To deploy monitoring stations into the observed network, we propose an influence-distance-based k -station selection method where the influence distance is a conceptual measurement that estimates the probability that a rumor-infected node can influence its uninfected neighbors. Accordingly, a greedy algorithm is developed to find the best k monitoring stations among all rumor-infected nodes with a 2-approximation. Based on the infection path, which is most likely toward the k infection monitoring stations, we derive that an estimator for the “most like” rumor source under the IC model is the Jordan infection center in a graph. Our theoretical analysis is presented in the article. The effectiveness of our method is verified through experiments over both synthetic and real-world datasets. As shown in the results, our k -station selection method outperforms off-the-shelf methods in most cases in the network under the IC model.

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Series: IEEE transactions on computational social systems
ISSN: 2373-7476
ISSN-E: 2329-924X
ISSN-L: 2329-924X
Issue: Online first
DOI: 10.1109/tcss.2023.3284909
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the National Science Foundation under Grant 1822985, in part by the National Natural Science Foundation of China under Grant 62202109, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110321 and 2022A1515010611, and in part by Guangzhou Basic and Applied Basic Research Foundation under Grant 202201010676.
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