Identify information sources with different start times in complex networks based on sparse observers  

作　　者：Yuan-Zhang Deng Zhao-Long Hu Feilong Lin Chang-Bing Tang Hui Wang Yi-Zhen Huang 邓元璋;胡兆龙;林飞龙;唐长兵;王晖;黄宜真(School of Computer Science and Technology,Zhejiang Normal University,Jinhua 321004,China;School of Physics and Electronic Information Engineering,Zhejiang Normal University,Jinhua 321004,China;School of Information Engineering,Jinhua Polytechnic,Jinhua 321016,China)

机构地区：[1]School of Computer Science and Technology,Zhejiang Normal University,Jinhua 321004,China [2]School of Physics and Electronic Information Engineering,Zhejiang Normal University,Jinhua 321004,China [3]School of Information Engineering,Jinhua Polytechnic,Jinhua 321016,China

出　　处：《Chinese Physics B》2024年第11期467-479,共13页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.62103375,62006106,61877055,and 62171413);the Philosophy and Social Science Planning Project of Zhejinag Province,China(Grant No.22NDJC009Z);the Education Ministry Humanities and Social Science Foundation of China(Grant No.19YJCZH056);the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LY23F030003,LY22F030006,and LQ21F020005).

摘　　要：The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial moments.Although there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains unresolved.In this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start times.The proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread duration.The core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear programming.Extensive experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and efficiency.Furthermore,we find that our method maintains robustness irrespective of the number of sources and the average degree of network.Compared with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.

关 键 词：complex networks information spread source identification backward spread centricity 

分 类 号：O157.5[理学—数学]