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Overlapping Community Detection Combining Topological Potential and Trust Value of Nodes

机译:结合节点的拓扑势和信任值的重叠社区检测

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Aiming at the problems of existing algorithms, such as instability, neglecting interaction between nodes and neglecting attributes of node, an overlapping community discovery algorithm combining topological potential and trust value of nodes was proposed. Firstly, the importance of nodes is calculated according to topological potential and the trust value of the node, and then K core nodes are selected. In final, the final division of communities are finished by using the extended modularity and core nodes. Experimental results on LFR network datasets and three real network datasets, verify the efficiency of the proposed OCDTT algorithm.
机译:针对现有算法不稳定,忽略节点间交互,忽略节点属性等问题,提出了一种结合节点拓扑势和信任值的重叠社区发现算法。首先根据拓扑势和节点的信任度来计算节点的重要性,然后选择K个核心节点。最后,通过使用扩展的模块化和核心节点来完成对社区的最终划分。在LFR网络数据集和三个真实网络数据集上的实验结果验证了所提出的OCDTT算法的有效性。

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