A novel approach for detecting multiple rumor sources in networks with partial observations

Zhao Zhang (), Wen Xu, Weili Wu and Ding-Zhu Du ()
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Zhao Zhang: Zhejiang Normal University
Wen Xu: University of Texas at Dallas
Weili Wu: University of Texas at Dallas
Ding-Zhu Du: Ton Duc Thang University

Journal of Combinatorial Optimization, 2017, vol. 33, issue 1, No 9, 132-146

Abstract: Abstract Locating source of information diffusion in networks has very important applications such as locating the sources of epidemics, news/rumors in social networks or online computer virus. In this paper, we consider detecting multiple rumor sources from a deterministic point of view by modeling it as the set resolving set (SRS) problem. Let G be a network on n nodes. A node subset K is an SRS of G if all detectable node sets are distinguishable by K. The problem of multiple rumor source detection (MRSD) in the network can be modeled as finding an SRS K with the smallest cardinality. In this paper, we propose a polynomial-time greedy algorithm for finding a minimum SRS in a general network, achieving performance ratio $$O(\ln n)$$ O ( ln n ) . This is the first work establishing a relation between the MRSD problem and a deterministic concept of SRS, and a first work to give the minimum SRS problem a polynomial-time performance-guaranteed approximation algorithm. Our framework suggests a robust and efficient approach for estimating multiple rumor sources independent of diffusion models in networks.

Keywords: Social network; Rumor source detection; Set resolving set; Approximation algorithm (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10878-015-9939-x

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