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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度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：图相似度一直是网络科学中的关键主题，并广泛用于网络动力学，图监视和异常事件检测。但是，很少有研究关注社区的相似性。社区不一定拥有子模块化结构这一事实决定了图相似性算法不能直接应用于社区。此外，现有的图相似度算法忽略了网络的组织结构。当两个顶点和结构相同时，可以认为它们是相同的。因此，现有算法无法检测到有关社区组织结构转变的异常事件。在本文中，我们提出了一种新颖的社区相似度算法，该算法既考虑了顶点的偏移又考虑了社区的分层结构的偏移。社区的分层结构根据节点在社区中的影响将节点分为不同的组。每个节点的影响和节点影响的转移都将影响两个社区的相似性。对合成数据的实验表明，该新算法的性能优于最新算法。此外，我们将新算法应用于科学数据集，并确定科学制图中发生的有意义的异常事件。事实证明，异常事件与期刊社区的主题转变相对应。结果表明，该新算法可以有效地检测出与社区结构转变有关的异常事件。

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