论文标题
稀疏感 - 强大的社区检测（Sparcode）
Sparsity-aware Robust Community Detection(SPARCODE)
论文作者
论文摘要
社区检测是指在图中找到密度连接的节点组。在诸如集群分析和网络建模之类的重要应用中，该图稀疏，但离群值和重尾噪声可能会掩盖其结构。我们提出了一种新方法，用于稀疏性强大的社区检测（Sparcode）。从密度连接和离群值腐败的图形开始，我们首先提取初步的稀疏性改进的图形模型，通过映射来自不同簇的坐标，从而优化稀疏度，以使其嵌入距离的距离最大。然后，除去不希望的边缘，并通过使用改进的图形模型中的节点的连接来检测异常值来稳健地构造图形。最后，在由此产生的可靠稀疏图模型上执行快速光谱分区。使用模块化优化在分区结果上估算社区的数量。我们将性能与流行图和基于群集的社区检测方法进行比较，并在各种基准网络和集群分析数据集上进行比较。 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.
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.
