异质信息网络的复杂条件社区搜索

社区是信息网络的重要属性, 社区搜索旨在寻找满足用户给定条件的节点集合, 是信息网络分析的重要研究内容. 异质信息网络由于包含更加全面、丰富的结构和语义信息, 所以异质信息网络的社区搜索近年来受到人们的广泛关注. 针对现有异质信息网络的社区搜索方法难以满足复杂条件社区搜索要求的不足, 定义了复杂条件社区搜索问题, 提出了考虑非对称元路径、受限元路径和禁止节点约束的搜索算法. 3种算法分别通过元路径补全策略、调整带标签的批量搜索策略和拆分复杂搜索条件的方式搜索社区, 同时针对禁止节点约束的搜索算法设计了基于剪枝策略和近似策略的优化算法以提高搜索效率. 在真实数据集上进行了大量实验, 实验结果证明了所提算法的有效性和高效性.

Complex Conditional Community Search over Heterogeneous Information Networks

Community is an important attribute of information networks. Community search, as an important content of information network analysis, aims to find a set of nodes that meet the conditions specified by the user. As heterogeneous information networks contain more comprehensive and richer structural and semantic information, community search in such networks has received extensive attention in recent years. However, the existing community search methods for heterogeneous information networks cannot be directly applied when the search conditions are complex. For this reason, this study defines community search under complex conditions and proposes search algorithms considering asymmetric meta-paths, constrained meta-paths, and prohibited node constraints. These three algorithms respectively use the meta-path completion strategy, the strategy of adjusting batch search with labeling, and the way of dividing complex search conditions to search communities. Moreover, two optimization algorithms respectively based on the pruning strategy and the approximate strategy are designed to improve the efficiency of the search algorithm with prohibited node constraints. A large number of experiments are performed on real datasets, and the experimental results verify the effectiveness and efficiency of the proposed algorithms.