Flexible community search approaches over attribute graph
Community search aims to search for communities that satisfy specified conditions,and has a wide range of applications in the real world.The problem of community search in attribute graphs is studied.Considering the need to limit the number of vertices in a community in practical applications,a flexible attribute community search problem is proposed,whose goal is to find the subgraph with maximum graph attribute scores among connected subgraphs containing the query node with limited node sizes.Different from the traditional community search problem,a parameter-free community model is adpoted to measure the closeness of the community,thus avoiding the difficulty of specifying parameters and making the query more flexible.Three algorithms are proposed,i.e.,exact algorithm EXACT,heuristic algorithm FACH and optimization algorithm FACH+.In FACH and FACH+,the research designs the pruning rule and modifies the heuristic strategy appropriately in FACH+,which can find the subgraphs that meet the requirements quickly and efficiently.The results of experiments on several real social network datasets show that the algorithms proposed in this paper have significant advantages in both accuracy and efficiency.
国家科技期刊平台
NSTL
万方数据
维普
