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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011
摘要
In this paper we extend our previous work on the 隨機 block model, a commonly used generative model for 社會 and 生物 網絡s, and the problem of inferring functional groups or communities from the topology of the 網絡. We use the cavity method of 統計 physics to obtain an asymptotically exact analysis of the phase diagram. We describe in detail properties of the detectability-undetectability 相變 and the easy-hard 相變 for the community detection problem. Our analysis translates naturally into a belief propagation algorithm for inferring the group memberships of the nodes in an optimal way, i.e., that maximizes the overlap with the underlying group memberships, and learning the underlying parameters of the block model. Finally, we apply the algorithm to two examples of real-world 網絡s and discuss its perfor
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