基于带权图的层次化社区并行计算方法

提出了一种基于带权图并行分解的层次化社区发现方法,该方法采用图划分的方式定义社区结构,并在这种社区结构之上实现了社会网络社区发现并行算法P-SNCD(parallel social network community discovery).P-SNCD算法有效地避免了传统的基于“模块度”的社区发现方法倾向于发现相似规模社区的弊端.同时,该算法能够以可扩展的方式,在处理器规模为O(hmn)或O(hn2)的条件下,以并行计算时间复杂度为O(logn)高效地挖掘大规模复杂社会网络中社区密度为h的社区,其中,n为社会网络节点数,m为边数,h为用户指定的任意社区密度.所提出的算法对用户参数输入要求简单,从而使得算法具有较强的实用性.充分的实验数据验证了所提出算法的精确性和高效性.

Parallel Computing Hierachical Community Approach Based on Weighted-Graph

This paper proposes a weighted-graph based hierarchical community detection approach,which defines the community structure with the use of graph partition.With the pre-defined structure,a novel parallel social network community discovery algorithm(P-SNCD for short) is designed.P-SNCD algorithm avoids the disadvantage of traditional modularity based methods,which tend to discover communities of similar scales.Moreover,it can efficiently mine communities in parallel with the CPU scale of O(hmn) or O(hn2) and time complexity of O(logn),where h represents the density of the communities,m represents the total number of links and n represents the total number of nodes.Compared to the most of the existing methods,P-SNCD algorithm requires a few input parameters makes it even more practical.The accuracy and effectiveness of our algorithm is guaranteed by sufficient empirical studies in the later sections.