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A Practical Method for Detecting Community Structures in Decentralized and Unstructured P2P Networks

机译:分散和非结构化P2P网络中检测社区结构的实用方法

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In decentralized and unstructured P2P networks like Gnutella, there is no coupling between topology and data location. Nodes can leave the network arbitrarily, which greatly affects their neighbors. We should determine in advance how many and which nodes will be affected when some nodes leave the network due to unstable Internet environment. In view of this challenge, we model the problem as detecting community structures, regarding the leaving nodes as community cores. Just as the flooding data queries in decentralized and unstructured P2P networks, the algorithm also works in a flooding way. It starts from the community cores and assign their neighbors to each corresponding community. Specifically, we focus on decentralized and unstructured P2P networks only. At last, a case study is presented for validating the method.
机译:在像Gnutella这样的分散式和非结构化P2P网络中,拓扑和数据位置之间没有耦合。节点可以任意离开网络,这极大地影响了它们的邻居。我们应该预先确定由于不稳定的Internet环境而导致某些节点离开网络时将影响多少个节点和哪些节点。鉴于这一挑战,我们将问题建模为检测社区结构,将离开节点视为社区核心。正如在分散式和非结构化P2P网络中查询泛洪数据一样,该算法也可以以泛洪方式工作。它从社区核心开始,并将其邻居分配给每个相应的社区。具体来说,我们仅关注分散和非结构化的P2P网络。最后,通过案例研究验证了该方法的有效性。

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