计算机科学 ›› 2022, Vol. 49 ›› Issue (11): 316-325.doi: 10.11896/jsjkx.211000043

• 信息安全 • 上一篇    下一篇

社交网络中的虚假信息经加边修正最大化问题

宋新月, 帅天平, 陈彬   

  1. 北京邮电大学理学院 北京 100876
  • 收稿日期:2021-10-08 修回日期:2022-04-22 出版日期:2022-11-15 发布日期:2022-11-03
  • 通讯作者: 帅天平(tpshuai@bupt.edu.cn)
  • 作者简介:(xysong@bupt.edu.cn)
  • 基金资助:
    国家自然科学基金(12171051,12171052);中央高校基本科研业务费(500421358)

Misinformation Correction Maximization Problem with Edge Addition in Social Networks

SONG Xin-yue, SHUAI Tian-ping, CHEN Bin   

  1. School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2021-10-08 Revised:2022-04-22 Online:2022-11-15 Published:2022-11-03
  • About author:SONG Xin-yue,born in 1998,postgra-duate.Her main research interests include combinatorial optimization and social network.
    SHUAI Tian-ping,born in 1977,Ph.D,associate professor.His main research interests include combinatorial optimization and social network.
  • Supported by:
    National Natural Science Foundation of China(12171051,12171052) and Fundamental Research Funds for the Central Universities of China(500421358).

摘要: 在线社交网络如微信等的普及,使人们更加关注信息传播的问题。虚假信息在社交网络中进行传播可能会造成很严重的后果,比如经济损失或者公众恐慌等。因此,需要采取相关的措施来控制虚假信息的传播。传统的虚假信息控制方法主要通过向网络中的部分节点传播真实信息,让真实信息和虚假信息进行竞争来减小虚假信息的影响。文中将传播真实信息和加边的方式相结合,提出了一个虚假信息修正最大化问题。该问题是NP-难的,其目标函数值的计算是#P-难的。由于目标函数既不是次模的也不是超模的,因此采用三明治近似策略来求解该问题。为此,构造目标函数的次模的上界和下界函数,利用反向影响采样技术在基数约束下求解上界和下界函数,最终得到原问题的一个数据相关的近似解。通过在3个真实网络的数据集上进行仿真实验,验证了所提算法的有效性。

关键词: 社交网络, 信息传播, 影响力最大化, 虚假信息控制, 三明治近似策略

Abstract: The popularity of online social networks such as Wechat has aroused people’s more attention to information diffusion.The spread of misinformation in social networks may lead to serious consequences,such as economic losses and public panic.Therefore,relevant measures need to be taken to control the spread of misinformation.The classical misinformation containment problem aims to reduce the impact of misinformation by launching a set of nodes as real information seeds to compete against misinformation.This paper combines the spread of real information with the edge addition,proposes a misinformation correction maximization problem.The proposed problem is NP-hard and the computation of its objective function is #P-hard.Then,we use the sandwich approximation strategy to get an approximation solution since the objective function is neither submodular nor supermodular.We first find submodular lower and upper bound functions of the objective function,and then get corresponding approximation solutions under the cardinality constraint by the reverse influence sampling technique.At last,combined with lower and upper bound function’s solution,we obtain an approximation solution for misinformation correction maximization problem with a data-dependent approximation ratio.Experiments on three realistic data sets indicate that the proposed algorithm is efficient and performs better than classical heuristic methods.

Key words: Social network, Information diffusion, Influence maximization, Misinformation containment, Sandwich approximation strategy

中图分类号: 

  • TP391
[1]KEMPE D,KLEINBERG J,TARDOS É.Maximizing the spread of infıuence through a social network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2003:137-146.
[2]CHEN W,WANG C,WANG Y J.Scalable influence maximization for prevalent viral marketing in large-scale social networks[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2010:1029-1038.
[3]BROGS C,BRAUTBAR M,CHAYES J,et al.Maximizing social infıuence in nearly optimal time [C]//Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms.SIAM,2014:946-957.
[4]TANG Y Z,XIAO X K,SHI Y C.Influence maximization:near-optimal time complexity meets practical efficiency[C]//Procee-dings of the 2014 ACM SIGMOD International Conference on Management of Data.ACM,2014:75-86.
[5]TANG Y Z,SHI Y C,XIAO X K.Influence Maximization in Near-Linear Time:A Martingale Approach[C]//Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data.ACM,2015:1539-1554.
[6]HU Q C,ZHANG Y,XING C X.K-clique Heuristic Algorithm for Influence Maximization in Social Network[J].Computer Science,2018,45(6):32-35.
[7]KONG F,LI Q Z,LI S.Survey on Online Influence Maximization [J].Computer Science,2020,47(5):7-13.
[8]TAN Q,ZHANG F L,ZHANG Z Y,et al.Modeling Methods of Social Network User Influence [J].Computer Science,2021,48(2):76-86.
[9]TAN Q,ZHANG F L,WANG T,et al.Social Network User Influence Evaluation Algorithm Integrating Structure Centrality [J].Computer Science,2021,48(7):124-129.
[10]DOERR B,FOUZ M,FRIEDRICH T.Why rumors spread so quickly in social networks [J].Communications of the ACM,2012,55(6):70-75.
[11]BUDAK C,AGRAWAL D,ABBADI E A.Limiting the spread of misinformation in social networks[C]//Proceedings of the 20th International Conference on World Wide Web.ACM,2011:665-674.
[12]HE X R,SONG G J,CHEN W,et al.Influence blocking maximization in social networks under the competitive linear thre-shold model[C]//Proceedings of the 2012 SIAM International Conference on Data Mining.SIAM,2012:463-474.
[13]TONG G M,WU W L,GUO L,et al.An efficient randomized algorithm for rumor blocking in online social networks[J].IEEE Transactions on Network Science and Engineering,2020,7(2):845-854.
[14]WANG S Z,ZHAO X J,CHEN Y,et al.Negative influence mi-nimizing by blocking nodes in social networks [C]//Proceedings of the 17th AAAI Conference on Late-Breaking Developments in the Field of Artificial Intelligence.ACM,2013:134-136.
[15]YAN R D,LI D Y,WU W L,et al.Negative influence minimizing by blocking nodes in social networks [J].IEEE Transactions on Network Science and Engineering,2020,7(3):1067-1078.
[16]KIMURA M,SAITO K,MOTODA H.Minimizing the spread of contamination by blocking links in a network[C]//Proceedings of the 23rd National Conference on Artificial Intelligence-Vo-lume 2.ACM,2008:1175-1180.
[17]KUHLMAN C J,TULI G,SWARUP S,et al.Blocking simple and complex contagion by edge removal [C]//IEEE 13th International Conference on Data Mining.IEEE,2013:399-408.
[18]CHEN J Y,ZHANG D J,LIN X,et al.False Message Propagation Suppression Based on Influence Maximization [J].Compu-ter Science,2020,47(S1):17-23.
[19]VOSOUGHI S,ROY D,ARAL S.The spread of true and false news online [J].Science,2018,359(6380):1146-1151.
[20]CHAOJI V,RANU S,RASTOGI R,et al.Recommendations to boost content spread in social networks[C]//Proceedings of the 21st International Conference on World Wide Web.ACM,2012:529-538.
[21]D’ANGELO G,SEVERINI L,VELAJ Y.Recommending links through influence maximization[J].Theoretical Computer Science,2019,764:30-41.
[22]YU L,LI G H,YUAN L.Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks[J].ACM/IMS Transactions on Data Science,2020,1(2):1-21.
[23]KARP R M.Reducibility among combinatorial problems[C]//Complexity of Computer Computations.Springer,1972:85-103.
[24]VALIANT L G.The complexity of enumeration and reliability problems [J].SIAM Journal on Computing,1979,8(3):410-421.
[25]LU W,CHEN W,LAKSHMANAN L V S.From competition to complementarity:Comparative influence diffusion and maximization [J].Proceedings of the VLDB Endowment,2015,9(2):60-71.
[26]ROSSI R A,AHMED N K.The network data repository with interactive graph analytics and visualization[EB/OL].http://networkrepositoy.com.
[27]GAO C,LIU J,ZHONG N.Network immunization and virus propagation in email networks experimental evaluation and ana-lysis[J].Knowledge and Information Systems,2011,27(2):60-71.
[28]KHALIL E,DIKINA B,SONG L.Scalable diffusion-aware optimization of network topology [C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.ACM,2014:1226-1235.
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