论文标题
在有导网络中用于社区检测的光谱算法
Spectral Algorithms for Community Detection in Directed Networks
论文作者
论文摘要
大型社交网络中的社区发现受节点的学位异质性的影响。引入了针对有向网络的D得分算法，以减少这种效果，通过在聚类之前采用邻接矩阵的奇异向量的元素比率。统计学家引用网络获得了有意义的结果，但缺少对其性能的严格分析。首先，本文为该算法建立了理论保证及其定向度校正模型（定向DCBM）的变体。其次，本文通过使用原始网络的信息而不是单数矢量将节点附加到社区内核之外，从而为原始的D分数算法提供了重大改进。
Community detection in large social networks is affected by degree heterogeneity of nodes. The D-SCORE algorithm for directed networks was introduced to reduce this effect by taking the element-wise ratios of the singular vectors of the adjacency matrix before clustering. Meaningful results were obtained for the statistician citation network, but rigorous analysis on its performance was missing. First, this paper establishes theoretical guarantee for this algorithm and its variants for the directed degree-corrected block model (Directed-DCBM). Second, this paper provides significant improvements for the original D-SCORE algorithms by attaching the nodes outside of the community cores using the information of the original network instead of the singular vectors.
