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Keywords:
weighted complex networks; community detection; overlapping community; disjoint community
weighted complex networks; community detection; overlapping community; disjoint community
Summary:
The excessive increase in the amount of data caused a rise in the number of vertices and edges in the graph models. This gave rise to the concept of complex networks. Complex networks are present in almost every area of life, in social networks, natural sciences, drug discovery, etc. Detection of overlapping or disjoint communities in complex networks is an important problem. In this study, we propose two new algorithms to detect overlapping and disjoint communities in weighted complex networks. We assume that the weights represent how close the vertices are to each other in some sense. First, we calculate the similarity of the vertices to each other using the universal gravitational law, then place similar vertices in overlapping communities. Then, for each vertex in multiple communities, we calculate the attraction force of each community where this vertex is located. We leave the vertex in the community that attracts the vertex more and delete it from the others. The results of experiments conducted on complex networks consisting of real and artificial data show the efficiency of the proposed algorithms.
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