摘要
While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.
作者
马志伟
孙蕾
丁智国
黄宜真
胡兆龙
Zhi-Wei Ma;Lei Sun;Zhi-Guo Ding;Yi-Zhen Huang;Zhao-Long Hu(Zhejiang Normal University,School of Computer Science and Technology,Jinhua 321004,China;Shanghai Business School,School of Business and Economics,Shanghai 200235,China;Jinhua Polytechnic,School of Information Engineering,Jinhua 321016,China)
基金
Project supported by the National Natural Science Foundation of China(Grant Nos.62103375 and 62006106)
the Zhejiang Provincial Philosophy and Social Science Planning Project(Grant No.22NDJC009Z)
the Education Ministry Humanities and Social Science Foundation of China(Grant Nos.19YJCZH056 and 21YJC630120)
the Natural Science Foundation of Zhejiang Province of China(Grant Nos.LY23F030003 and LQ21F020005).
