异质网络中基于关键词属性的Truss社区搜索.

Community search,as an important research content of social network analysis,aims to find densely connected subgraphs that highly relate to the query node given by users.Most community search methods currently available focus on homogeneous networks.However,in reality,information networks are often attribute-heterogeneous.This paper proposed P-distance and S-distance based on meta-path P and meta-structure S in heterogeneous networks,as well as (k,d,P) -truss and (k,d,S) -truss community models,to measure the structural cohesion of subgraphs.Additionally,it proposed a keyword attribute score function to measure the keyword attribute correlation of different subgraphs,and presented algorithms which could find communities with the highest keyword attribute score of (k,d,P) -truss and (k,d,S) -truss.Search algorithms could find a personalized community with both structural cohesion and keyword attribute correlation,and support to limit the maximum distance d between the query node and any node in the community to control the scope of community search.Compared with the related community search algorithms on real-world datasets,the experimental results prove the effectiveness and feasibility of the proposed algorithms. [ABSTRACT FROM AUTHOR]