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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014
摘要
Bipartite 網絡s are a common type of 網絡 data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite 網絡s exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of 資訊 through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite 網絡s by formulating a bipartite 隨機 block model, which explicitly includes vertex type 資訊 and may be trivially extended to k-partite 網絡s. This bipartite 隨機 block model yields a projection-free and 統計ly principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstr
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