异质网络中基于关键词属性的Truss社区搜索  

Truss community search based on keyword attributes over heterogeneous networks

作　　者：杨成波 周丽华[1] 黄亚群[1] 杨宇迪 Yang Chengbo;Zhou Lihua;Huang Yaqun;Yang Yudi(School of Information Science&Engineering,Yunnan University,Kunming 650500,China;Dianchi College of Yunnan University,Kunming 650228,China)

机构地区：[1]云南大学信息学院,昆明650500 [2]云南大学滇池学院,昆明650228

出　　处：《计算机应用研究》2023年第6期1708-1714,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(62062066,61762090,61966036,62276227);云南省基础研究计划重点资助项目(202201AS070015);云南省智能系统与计算重点实验室资助项目(202205AG070003)。

摘　　要：社区搜索旨在信息网络中寻找与用户指定的查询节点高度相关的稠密连通子图,是社会网络分析的重要研究内容。现有的社区搜索方法大多是针对同质网络,但现实中的信息网络通常是包含多种节点类型和多种关系类型的属性异质网络。提出了异质网络中基于元路径P和元结构S的P-距离和S-距离及(k,d,P)-truss和(k,d,S)-truss社区模型以度量子图的结构内聚性,同时提出了关键词属性得分函数用于度量不同子图的关键词属性相关性,最后提出了搜索具有最高关键词属性得分的(k,d,P)-truss和(k,d,S)-truss的社区搜索算法。搜索算法能够找到同时具有结构内聚性和关键词属性相关性的个性化社区,并且支持限制查询节点与社区内任意节点的最大距离d来控制社区搜索的范围。在真实数据集上与相关的社区搜索算法进行了实验对比,结果证明了所提算法的有效性和可行性。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.

关 键 词：异质网络 TRUSS 社区搜索 关键词属性 元路径 元结构 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]