Searching the community containing a given query node in heterogeneous information networks(HINs)has a wide range of application values,such as friend recommendation,epidemic monitoring and so on.However,most of the existing HINs community search methods impose strict requirements on the topology of the community based on the predefined subgraph pat-tern,ignoring the attribute similarity between nodes,which will be difficult to locate the community with weak structural relation-ship and high attribute similarity.And the global search mode is difficult to effectively deal with large-scale network data.To solve these problems,we design disentangled graph neural network and the local modularity based on meta path to measure the attribute similarity and structural cohesion between nodes respectively.Moreover,we use the 0/1 knapsack problem to optimize the impact of the attribute and structure on the community,define the most valuable c-size community search problem,and then propose a value maximization community search algorithm based on disentangled graph neural network to perform a three-stage search process.In the first stage,we construct candidate subgraphs according to the query in-formation and meta-path,control the search range within the local range of the query vertex to ensure the search efficiency of the whole algorithm.In the second stage,we use the disentangled graph neural network to fuse the heterogeneous information and user label information to calculate the at-tribute similarity between nodes.In the third stage,we design a greedy algorithm to find the c-size community with high attribute similarity and structural cohesion according to the community definition and cohesion measurement indicator.Finally,we test the performance of algorithm on real homogeneous and heterogeneous data sets,and a large number of experimental results demon-strate the effectiveness and efficiency of the proposed model.
关键词
异质信息网络/社区搜索/解耦图神经网络/元路径/局部模块度
Key words
Heterogeneous information networks/Community search/Disentangled graph neural network/Meta-paths/Local mo-dularity
维普
万方数据