Weighted line graphs for overlapping community discovery
Weighted line graphs for overlapping community discovery
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DOI：
10.1007/s13278-013-0104-1
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发表时间：
2013-03
影响因子：
2.8
通讯作者：
Tetsuya Yoshida
中科院分区：
文献类型：
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作者：
Tetsuya Yoshida
我们提出了用于重叠社区发现的加权线图，其中网络中的节点可以分配给多个社区。对于没有自环的无向连接网络，我们通过以下方式提出加权线图：（1）根据原始网络中的权重定义线图的权重，以及（2）删除加权线图中的自环，同时保持其属性。通过对变换后的图应用一些现成的节点划分方法，将相邻链路的社区标签分配给原始网络中的每个节点。在合成网络和现实网络上进行了实验，结果表明所提出的方法可以提高发现的重叠社区的质量。
We propose weighted line graphs for overlapping community discovery where a node in a network can be assigned to more than one community. For undirected connected networks without self-loops, we propose weighted line graphs by: (1) defining weights of a line graph based on the weights in the original network, and (2) removing self-loops in weighted line graphs, while sustaining their properties. By applying some off-the-shelf node partitioning method to the transformed graph, community labels of adjacent links are assigned to each node in the original network. Experiments are conducted over both synthetic and real-world networks, and the results indicate that the proposed approach can improve the quality of discovered overlapping communities.