Research On Community Discovery Method Based On Attention Mechanism In Heterogeneous Information Network

Community discovery is an important research content in complex network analysis,which is widely used in recommendation system,advertising and public opinion monitoring.But now most of the research on community discovery methods are based on the homogeneous information network,that is,all the nodes in the network are regarded as a type.But most of the network types in real life are heterogeneous information network,that is,the nodes and edges in the network belong to the same type.In recent years,the research of heterogeneous information network has been widely concerned by the academic community.In order to better study the heterogeneous information network,this paper makes an in-depth study on the community discovery algorithm in heterogeneous information network.The specific research contents include:1.Similarity measurement: in order to measure the similarity between points more accurately,firstly,the similarity between two adjacent nodes is obtained by information divergence,and the weight of the meta path under the corresponding node is obtained by combining the attention mechanism.Then the two are weighted and fused,and a new node similarity measurement method is proposed.2.Combined with the improved k-means clustering algorithm for community discovery: the traditional K-means clustering algorithm to obtain the cluster center is the method of random selection,the accuracy of cluster center selection is very important,inappropriate cluster center will lead to community division results error.Based on this,combined with the new similarity measurement algorithm,this paper proposes a new method to select the cluster center,no longer randomly select the cluster center,so as to make the partition result of community discovery more accurate.