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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011
摘要
We present an asymptotically exact analysis of the problem of detecting communities in sparse random 網絡s generated by 隨機 block models. Using the cavity method of 統計 physics and its relationship to belief propagation, we unveil a 相變 from a regime where we can infer the correct group assignments of the nodes to one where these groups are undetectable. Our approach yields an optimal inference algorithm for detecting modules, including both assortative and disassortative functional modules, assessing their significance, and learning the parameters of the underlying block model. Our algorithm is scalable and applicable to real-world 網絡s, as long as they are well described by the block model.
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