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 Contain-ment,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.
Two Stage Rumor Blocking Method Based on EHEM in Social Networks
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 ex-tended heat energy model(EHEM),to characterize information propagation.EHEM fully takes into consideration several key as-pects 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 algo-rithms,including Random,Betweenness,MD,PR,PWD,and ContrId on multiple metrics.
Information propagationSocial networksRumor blockingInfluence minimizationRumor blocking strategies
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