异质信息网络的互信息最大化社区搜索

Community search with mutual information maximization over heterogeneous information networks

针对现有社区搜索方法难以处理复杂多样的搜索要求及在高维稀疏的异质信息网络(HINs)中难以融合网络结构和节点属性来度量节点间相关性的不足,提出异质信息网络互信息最大化社区搜索问题,给出互信息最大化的社区定义,设计相应的搜索方法 (互信息最大化社区搜索,CSMIM).将用户的搜索要求定义为查询约束,利用带查询约束的深度图互信息最大化(QC-DGI)模型融合网络结构、语义和节点属性信息获得节点嵌入,有效地计算节点间的互信息.根据给定的查询信息,利用互信息最大化准则搜索目标社区.为了提高搜索结果的准确率,提出基于用户反馈的优化策略,实现互信息从全局到局部的个性化计算.在真实数据集上进行大量实验,实验结果表明所提方法能够有效地根据搜索要求挖掘出给定节点所在的社区,相比具有代表性的基线方法有更高的准确率.

Existing community search methods are difficult to deal with the complex and diverse search requirements of users and difficult to integrate network structure and node attributes to measure the correlation between nodes in high-dimensional and sparse heterogeneous information networks(HINs). In order to solve the problems, the community search problem of mutual information maximization over HINs was proposed, the definition of community with mutual information maximization was given, and a corresponding community search method,community search with mutual information maximization(CSMIM) was designed. The user’s search requirements were defined as query constraints, and a query constraint deep graph infomax(QC-DGI) model was proposed to learn node embedding by extracting the structure, semantics and node attribute information in HINs to effectively calculate the mutual information between nodes. Then, according to the given query information, the mutual information maximization criterion was used to search the target community. In addition, an optimization strategy based on user feedback was proposed to realize the personalized calculation of mutual information from global to local, so as to improve the accuracy of search results. Finally, extensive experiments on real HINs dataset were performed, and experimental results prove that the proposed method can effectively search the community of a given node according to the search requirements, and has higher accuracy than the representative baseline methods.