稀疏感知的鲁棒社区检测（SPARCODE）
Sparsity-aware Robust Community Detection(SPARCODE)
摘要：
社区检测是指在图中找到密集连接的节点组。在重要的应用中，如聚类分析和网络建模，图是稀疏的，但离群值和重尾噪声可能会掩盖其结构。提出了一种新的稀疏感知的鲁棒社区检测方法（SPARCODE）。从一个密集连接和离群损坏的图开始，我们首先提取一个初步的稀疏改进的图模型，在这个模型中，我们通过映射来自不同聚类的坐标来优化稀疏度，使得它们的嵌入距离最大。然后，不需要的边缘被删除，并通过使用改进的图模型中的节点的连通性来检测离群点来鲁棒地构建图。最后，快速谱分割上得到的鲁棒稀疏图模型进行。社区的数量估计使用模块化优化的分区结果。我们比较流行的图形和基于集群的社区检测方法在各种基准网络和聚类分析数据集的性能。综合实验表明，我们的方法始终找到正确的社区数量，并优于现有的方法在检测性能，鲁棒性和模块化得分，同时需要一个合理的计算时间。
英文摘要：
Community detection refers to finding densely connected groups of nodes in graphs. In important applications, such as cluster analysis and network modelling, the graph is sparse but outliers and heavy-tailed noise may obscure its structure. We propose a new method for Sparsity-aware Robust Community Detection (SPARCODE). Starting from a densely connected and outlier-corrupted graph, we first extract a preliminary sparsity improved graph model where we optimize the level of sparsity by mapping the coordinates from different clusters such that the distance of their embedding is maximal. Then, undesired edges are removed and the graph is constructed robustly by detecting the outliers using the connectivity of nodes in the improved graph model. Finally, fast spectral partitioning is performed on the resulting robust sparse graph model. The number of communities is estimated using modularity optimization on the partitioning results. We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets. Comprehensive experiments demonstrate that our method consistently finds the correct number of communities and outperforms existing methods in terms of detection performance, robustness and modularity score while requiring a reasonable computation time.
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