BGLL-Based Attribution Overlap Community Discovery Algorithm

This paper mainly proves the feasibility and validity of the overlapping community discovery through the size of attribution. The MB algorithm based on BGLL algorithm is proposed. Firstly, karate data sets are divided based on the traditional community partition algorithm BGLL algorithm. After obtaining the result of partition, the nodes in each sub-community are used to find the corresponding degree of ownership of nodes. The size of each node's membership is then compared to determine which community the node belongs to. Finally, the threshold value is adjusted by modularity to determine whether the nodes under this belongingness are overlapping nodes until the module degree is maximum. The experimental results show that it is feasible to overlap the community nodes with the degree of belonging, and it has certain efficiency of algorithm optimization.