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Variational community partition with novel network structure centrality prior

机译：具有新颖网络结构中心性的变分社区划分

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In this paper, we proposed a novel two-stage optimization method for network community partition, which is based on inherent network structure information. The introduced optimization approach utilizes the new centrality measure of both network links and vertices to construct the key affinity description of the given network, while the direct similarities between graph nodes or nodal features are not available to obtain the classical affinity matrix. Indeed, such calculated network centrality information not only encodes the essential hints for detecting meaningful network communities, but also introduces a 'confidence' criterion for referencing new labeled benchmark nodes. For the resulted challenging combinatorial optimization problem of graph clustering, the proposed optimization method iteratively employs an efficient convex optimization algorithm which is developed based under a new variational perspective of primal and dual. Experiments over both artificial and real-world network datasets demonstrate that the proposed optimization strategy of community partition significantly improves results and outperforms the state-of-the-art algorithms in terms of accuracy and robustness, while using much less annotated benchmark information. (C) 2019 Published by Elsevier Inc.
机译：在本文中，我们提出了一种基于固有网络结构信息的新型两阶段网络社区划分优化方法。引入的优化方法利用网络链接和顶点的新集中度度量来构造给定网络的关键亲和力描述，而图节点或节点特征之间的直接相似性无法获得经典的亲和力矩阵。确实，这样计算出的网络中心信息不仅编码用于检测有意义的网络社区的基本提示，而且还引入了“可信”标准来引用新的标记基准节点。针对由此产生的具有挑战性的图聚类组合优化问题，所提出的优化方法迭代地采用了一种有效的凸优化算法，该算法是基于新的原始和对偶变分观点而开发的。在人工和真实网络数据集上的实验表明，所提出的社区划分优化策略在准确性和鲁棒性方面显着改善了结果，并且优于最新的算法，同时使用的注释信息却少得多。 （C）2019由Elsevier Inc.发布

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