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原文連結
論文資訊
- 類型：已發表論文
- 日期：2013-11-25
摘要
Spectral algorithms are classic approaches to clustering and community detection in 網絡s. However, for sparse 網絡s the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other algorithms such as belief propagation can do so. Here, we present a class of spectral algorithms based on a nonbacktracking walk on the directed edges of the graph. The spectrum of this operator is much better-behaved than that of the adjacency matrix or other commonly used matrices, maintaining a strong separation between the bulk eigenvalues and the eigenvalues relevant to community structure even in the sparse case. We show that our algorithm is optimal for graphs generated by the 隨機 block model, detecting communities all of the way down to the theo
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