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A Structural Based Community Similarity Algorithm and Its Application in Scientific Event Detection

机译：基于结构的社区相似性算法及其在科学事件检测中的应用

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摘要

Graph similarity has been a crucial topic in network science, and is widely used in network dynamics, graph monitoring and anomalous event detection. However, few studies have paid attention to community similarity. The fact that communities do not necessarily own sub-modularity structure determines that graph similarity algorithms can not be applied to communities directly. Besides, the existing graph similarity algorithms ignore the organization structure of networks. Two communities can be regarded as the same when both their vertices and structure are identical. Thus the existing algorithms are unable to detect anomalous events about the shift of communities' organization structure. In this paper, we propose a novel community similarity algorithm, which considers both the shift of vertices and the shift of communities' layered structure. The layered structure of communities categorizes nodes into different groups, depending on their influence in the community. Both the influence of each node and the shift of nodes' influence are expected to affect the similarity of two communities. Experiments on the synthetic data show that the novel algorithm performs better than the state-of-art algorithms. Besides, we apply the novel algorithm on the scientific data set, and identify meaningful anomalous events occurred in scientific mapping. The anomalous events are proved to correspond to the transition of topics for journal communities. It demonstrates that the novel algorithm is effective in detecting the anomalous events about the transition of communities' structure.
机译：图表相似性是网络科学的重要主题，广泛用于网络动态，图形监测和异常事件检测。然而，很少有研究则注意社区的相似性。社区不一定拥有子模块化结构的事实确定了图形相似度算法不能直接应用于社区。此外，现有的图形相似性算法忽略了网络的组织结构。当它们的顶点和结构都相同时，两个社区可以被视为相同的。因此，现有算法无法检测关于社区组织结构的转变的异常事件。在本文中，我们提出了一种新颖的社区相似性算法，其考虑了顶点的偏移和社区分层结构的偏移。根据其在社区的影响，社区的分层结构将节点分类为不同的组。预期每个节点的影响和节点的影响的影响会影响两个社区的相似性。合成数据的实验表明，新型算法比现有技术算法更好。此外，我们在科学数据集上应用新颖的算法，并识别科学映射中发生的有意义的异常事件。证明了异常事件对应于期刊社区的主题过渡。它表明，新颖的算法有效地检测关于社区结构过渡的异常事件。

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