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原文連結
論文資訊
- 類型：已發表論文
- 日期：2016-07-13
摘要
The detection of communities within a dynamic 網絡 is a common means for obtaining a coarse-grained view of a 複雜系統 and for investigating its underlying processes. While a number of methods have been proposed in the 機器學習 and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic 網絡s. Specifically, we analyze the limits of detectability for a dynamic 隨機 block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communitie
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