Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method ( ), which mainly consists of a multiple-neighbor range algorithm ( ) and a superposition similarity fluctuation algorithm ( ). introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently, maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on and , not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.

中文翻译：

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在广义动态社交网络中识别和评估基于异常结构变化的节点

近来，动态社交网络研究引起了极大的关注，特别是在分析动态社交网络演化过程中的异常变化的异常分析领域。然而，目前的研究大多集中在动态社交网络宏观表征的异常分析上，而未能在微观层面分析结构变化异常的节点。为了识别和评估仅具有有限结构信息的广义动态社会网络中基于异常结构变化的节点，本研究考虑了无向和无权图，并开发了一种多邻域叠加相似度方法。 )，主要由多邻域算法( ）和叠加相似度波动算法（ ）。 引入观察节点，表征多邻域内节点的结构相似度，并在外延相似度指标的基础上提出了一种新的多邻域相似度指标。随后， 最大限度地反映了每个节点的结构变化，从多样的多邻相似性的角度，使用了一个新的叠加相似性波动指数。结果，基于 和 , 不仅通过检测节点的异常结构变化来识别基于异常结构变化的节点，而且通过量化这些变化来评估它们的异常程度。通过广泛的实验与最先进的方法进行比较获得的结果表明， 可以准确识别基于结构变化的异常节点并很好地评估其异常程度。