至目前為止有相當多的研究領域在辨認網絡中的群聚(community)，像是全球資訊網(World Wide Web)，大型社群網絡，要求一個演算法完全完成群聚的定義是不切實際的。
因此，一系列的分散式群聚偵測演算法被提了出來，然而這些演算法的效能仍然有進步的空間。在這篇論文裡，我在局部社群結構(local community structure)的基礎上提出了一套新的演算法。我們使用合成和真實世界的網路資訊，如美式足球比賽，並將結果跟以往的分散式群聚偵測演算法做比較，實驗結果驗證了我們演算法的有效性。

There has been much recent interest on identifying communities in networks. For some networks, e.g. the World Wide Web, large online social networks, algorithms that require complete network information is impractical. Therefore, a number of distributed community detection algorithms have been proposed. However, their performance still leaves something to be desired. In this paper, we propose a new distributed community detection algorithm based on local community structure. We compare our results with previous methods experimentally using both synthetic and real world network datasets such as the National Collegiate Athletic Association (NCAA) football. Experimental results verify the effectiveness of our approach.

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