计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (7): 156-166.doi: 10.11896/jsjkx.230800169

• 数据库&大数据&数据科学 • 上一篇    下一篇

社交网络中基于EHEM的两阶段谣言抑制方法

刘维, 吴飞, 郭震, 陈崚   

  1. 扬州大学信息工程学院 江苏 扬州 225000
  • 收稿日期:2023-08-25 修回日期:2023-12-15 出版日期:2024-07-15 发布日期:2024-07-10
  • 通讯作者: 刘维(yzliuwei@126.com)
  • 基金资助:
    国家自然科学基金(61971233,61702441)

Two Stage Rumor Blocking Method Based on EHEM in Social Networks

LIU Wei, WU Fei, GUO Zhen, CHEN Ling   

  1. College of Information Engineering,Yangzhou University,Yangzhou,Jiangsu 225000,China
  • Received:2023-08-25 Revised:2023-12-15 Online:2024-07-15 Published:2024-07-10
  • About author:LIU Wei,born in 1982,professor,Ph.D supervisor,is a member of CCF(No.26190M).Her main research interests include data mining and complex network analysis.
  • Supported by:
    National Natural Science Foundation of China(61971233,61702441).

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摘要： 在线社交网络的兴起带来了一系列的挑战与风险,其中包括虚假以及恶意谣言的传播,这可能会误导民众,破坏社会的稳定。因此,对谣言的传播进行抑制成为当前社交网络领域的热点问题。目前已经积累较多谣言抑制的工作,但是还存在模型不能准确描述信息在社交网络上传播的问题,因此提出了一种新的刻画信息传播的模型——扩展热量模型(Extended Heat Energy Model,EHEM)。该模型充分考虑了信息传播中节点激活概率的动态调整机制、信息传播的持续级联机制以及节点状态的动态转变机制,更加精准地捕捉了信息在网络上传播的爆炸性和复杂性;其次,考虑到在真实世界相信谣言的节点在接触真相后存在将信仰转变到相信真相的可能性,提出了校正阈值来确定节点是否会发生信仰的转换;节点的重要程度决定了它们自身的影响力,因此还提出了节点多维质量来衡量节点的重要程度;最后提出了两阶段的谣言抑制(Two Stage Rumor Containment,TSRC)算法,该算法首先使用节点多维质量对网络进行剪枝处理,之后通过模拟的方式从网络中选出最优的正种子集合。在4个真实数据集上进行实验,结果表明,所提算法在多个指标上优于Random,Betweenness,MD,PR,PWD和ContrId这6种对比算法。

关键词: 信息传播, 社交网络, 谣言抑制, 影响力最小化, 谣言抑制策略

Abstract: Therise of online social networks has brought about a series of challenges and risks,including the spread of false and malicious rumors,which can mislead the public and disrupt social stability.Therefore,blocking the spread of rumors has become a hot topic in the field of social networks.While significant efforts have been made in rumor blocking,there still exist limitations in accurately describing information propagation in social networks.To address this issue,this paper proposes a novel model,the extended heat energy model(EHEM),to characterize information propagation.EHEM fully takes into consideration several key aspects of information propagation,including the dynamic adjustment mechanism of node activation probabilities,the cascading mechanism of information propagation,and the dynamic transition mechanism of node states.By incorporating these factors,the EHEM provides a more precise representation of the explosive and complex nature of information propagation.Furthermore,ta-king into account the possibility of belief transition from rumors to truth for nodes that initially believe in rumors in the real world,this paper introduces a correction threshold to determine whether a node undergoes belief transformation.Additionally,the importance of nodes determines their influence spreading.Therefore,a multidimensional quality measure of nodes is proposed to assess their importance.Finally,a two stage rumor containment(TSRC) algorithm is proposed,which first prunes the network using the multidimensional quality measure of nodes and then selects the optimal set of positive seeds through simulations.Expe-rimental results on four real-world datasets demonstrate that the proposed algorithm outperforms six other comparative algorithms,including Random,Betweenness,MD,PR,PWD,and ContrId on multiple metrics.

Key words: Information propagation, Social networks, Rumor blocking, Influence minimization, Rumor blocking strategies

中图分类号: 

  • TP399
[1]LIU M,NIE L,WANG X,et al.Online data organizer:micro-video categorization by structure-guided multimodal dictionary learning[J].IEEE Transactions on Image Processing,2018,28(3):1235-1247.
[2]KEMPE D,KLEINBERG J,TARDOS É.Maximizing the spread of influence through a social network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2003:137-146.
[3]HE X,SONG G,CHEN W,et al.Influence blocking maximi-zation in social networks under the competitive linear threshold model[C]//Proceedings of the 2012 SIAM International Confe-rence on Data Mining.Society for Industrial and Applied Mathematics,2012:463-474.
[4]ZHANG H,ZHANG H,LI X,et al.Limiting the spread of misinformation while effectively raising awareness in social networks[C]//Computational Social Networks:4th International Conference(CSoNet 2015).Beijing,China,Springer Inter-national Publishing,2015:35-47.
[5]TONG G,WU W,TANG S,et al.Adaptive influence maximization in dynamic social networks[J].IEEE/ACM Transactions on Networking,2016,25(1):112-125.
[6]TRIPATHI R,RAO S.Rumor containment in peer-to-peer mes-sage sharing online social networks[J].International Journal of Data Science and Analytics,2022,13(3):185-198.
[7]RICHARDSON M,DOMINGOS P.Mining knowledge-sharingsites for viral marketing[C]//Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2002:61-70.
[8]DOMINGOS P,RICHARDSON M.Mining the network value of customers[C]//Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2001:57-66.
[9]KEMPE D,KLEINBERG J,TARDOS É.Influential nodes in a diffusion model for social networks[C]//Automata,Languages and Programming:32nd International Colloquium(ICALP 2005).Lisbon,Portugal,Springer Berlin Heidelberg,2005:1127-1138.
[10]BUDAK C,AGRAWAL D,EL ABBADI A.Limiting the spread of misinformation in social networks[C]//Proceedings of the 20th International Conference on World Wide Web.2011:665-674.
[11]WANG S,ZHAO X,CHEN Y,et al.Negative influence minimizing by blocking nodes in social networks[C]//Proceedings of the 17th AAAI Conference on Late-Breaking Developments in the Field of Artificial Intelligence.2013:134-136.
[12]TANINMIŞ K,ARAS N,ALTİNELI K,et al.Minimizing the misinformation spread in social networks[J].Iise Transactions,2020,52(8):850-863.
[13]PHAM C V,THAI M T,DUONG H V,et al.Maximizing misinformation restriction within time and budget constraints[J].Journal of Combinatorial Optimization,2018,35(4):1202-1240.
[14]PHAM C V,PHU Q V,HOANG H X,et al.Minimum budget for misinformation blocking in online social networks[J].Journal of Combinatorial Optimization,2019,38:1101-1127.
[15]WANG B,CHEN G,FU L,et al.Drimux:Dynamic rumor in-fluence minimization with user experience in social networks[J].IEEE Transactions on Knowledge and Data Engineering,2017,29(10):2168-2181.
[16]KIMURA M,SAITO K,MOTODA H.Blocking links to minimize contamination spread in a social network[J].ACM Tran-sactions on Knowledge Discovery from Data(TKDD),2009,3(2):1-23.
[17]KHALIL E,DILKINA B,SONG L.Cuttingedge:Influence minimization in networks[C]//Proceedings of Workshop on Frontiers of Network Analysis:Methods,Models,and Applications at NIPS.2013:1-13.
[18]ZAREIE A,SAKELLARIOU R.Rumour spread minimization in social networks:A source-ignorant approach[J].Online Social Networks and Media,2022,29:100206.
[19]WANG X,DENG K,LI J,et al.Targeted influence minimization in social networks[C]//Advances in Knowledge Discovery and Data Mining:22nd Pacific-Asia Conference,PAKDD 2018,Melbourne,VIC,Australia,June 3-6,2018,Proceedings,Part III 22.Springer International Publishing,2018:689-700.
[20]YAN R,LI Y,WU W,et al.Rumor blocking through online link deletion on social networks[J].ACM Transactions on Know-ledge Discovery from Data(TKDD),2019,13(2):1-26.
[21]SAXENA A,HSU W,LEE M L,et al.Mitigating misinformation in online social network with top-k debunkers and evolving user opinions[C]//Companion Proceedings of the Web Confe-rence 2020.2020:363-370.
[22]SRINIVASAN S,LD D B.A social immunity based approach to suppress rumors in online social networks[J].International Journal of Machine Learning and Cybernetics,2021,12:1281-1296.
[23]HOSNI A I E,LI K,AHMAD S.Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors[J].Information Sciences,2020,512:1458-1480.
[24]TONG G,WU W,GUO L,et al.An efficient randomized algorithm for rumor blocking in online social networks[J].IEEE Transactions on Network Science and Engineering,2017,7(2):845-854.
[25]YE S,WANG J,FAN H.Minimize social network rumors based on rumor path tree[J].IEEE Access,2020,8:167620-167630.
[26]YANG L,LI Z,GIUA A.Containment of rumor spread in complex social networks[J].Information Sciences,2020,506:113-130.
[27]HE Q,ZHANG D,WANG X,et al.Graph convolutional network-based rumor blocking on social networks[J].IEEE Transactions on Computational Social Systems,2023,10(5):2244-2253.
[28]HOSNI A I E,LI K.Minimizing the influence of rumors during breaking news events in online social networks[J].Knowledge-Based Systems,2020,193:105452.
[29]DING X,LI M,TIAN Y,et al.RBOTUE:Rumor Blocking Considering Outbreak Threshold and User Experience[J/OL].IEEE Transactions on Engineering Management,2021:1-19.https://ieeexplore.ieee.org/abstract/document/9552197.
[30]WANG C,WANG G,LUO X,et al.Modeling rumor propaga-tion and mitigation across multiple social networks[J].Physica A:Statistical Mechanics and its Applications,2019,535:122240.
[31]WEN S,JIANG J,XIANG Y,et al.To shut them up or to clarify:Restraining the spread of rumors in online social networks[J].IEEE Transactions on Parallel and Distributed Systems,2014,25(12):3306-3316.
[32]ZHU J,NI P,WANG G,et al.Misinformation influence minimization problem based on group disbanded in social networks[J].Information Sciences,2021,572:1-15.
[33]BRANDES U.On variants of shortest-path betweenness cen-trality and their generic computation[J].Social Networks,2008,30(2):136-145.
[34]BRIN S,PAGE L.The anatomy of a large-scale hypertextualweb search engine[J].Computer Networks and ISDN Systems,1998,30(1/2/3/4/5/6/7):107-117.
[35]MANOUCHEHRI M A,HELFROUSH M S,DANYALI H.A theoretically guaranteed approach to efficiently block the influence of misinformation in social networks[J].IEEE Transactions on Computational Social Systems,2021,8(3):716-727.
[36]BONACICH P.Power and centrality:A family of measures[J].American Journal of Sociology,1987,92(5):1170-1182.
[37] BATAGELJ V,ZAVERSNIK M.Fast algorithms for determining(generalized) core groups in social networks[J].Advances in Data Analysis and Classification,2011,5(2):129-145.
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