In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglo...In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglomerating nodes into local communities,and the overlapping nodes are detected based on the idea of making each community strong.We propose a contribution coefficient bvcito measure the contribution of an overlapping node to each of its belonging communities,and the fuzzy coefficients of the overlapping node can be obtained by normalizing the bvci to all its belonging communities.The running time of our method is analyzed and varies linearly with network size.We investigate our method on the computergenerated networks and real networks.The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients.Furthermore,the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51278101 and 51578149)the Science and Technology Program of Ministry of Transport of China(Grant No.2015318J33080)+1 种基金the Jiangsu Provincial Post-doctoral Science Foundation,China(Grant No.1501046B)the Fundamental Research Funds for the Central Universities,China(Grant No.Y0201500219)
文摘In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglomerating nodes into local communities,and the overlapping nodes are detected based on the idea of making each community strong.We propose a contribution coefficient bvcito measure the contribution of an overlapping node to each of its belonging communities,and the fuzzy coefficients of the overlapping node can be obtained by normalizing the bvci to all its belonging communities.The running time of our method is analyzed and varies linearly with network size.We investigate our method on the computergenerated networks and real networks.The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients.Furthermore,the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.
