Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities with high-importance vertices has recently received a great deal of attention. However, existing works mainly focus on conventional homogeneous networks where vertices are of the same type, but are not applicable to heterogeneous information networks (HINs) composed of multi-typed vertices and different semantic relations, such as bibliographic networks. In this paper, we study the problem of high-importance community search in HINs. A novel community model is introduced, named heterogeneous significant community (HSC), to unravel the closely connected vertices of the same type with high attribute values through multiple semantic relationships. An HSC not only maximizes the exploration of indirect relationships across entities of the anchor-type but incorporates their significance. To search the HSCs, we first develop online algorithms by exploiting both segmented-based meta-path expansion and significance increment. Specially, a solution space reuse strategy based on structural nesting is designed to boost the efficiency. In addition, we further devise a two-level index to support searching HSCs in optimal time, based on which a space-efficient compact index is proposed. Extensive experiments on real-world large-scale HINs demonstrate that our solutions are effective and efficient for searching HSCs, and the index-based algorithms are 2-4 orders of magnitude faster than online algorithms.

翻译：社区搜索是一种个性化社区发现问题，旨在寻找包含查询顶点的紧密连接子图。其中，寻找包含高重要性顶点的社区近期受到广泛关注。然而，现有工作主要聚焦于传统同质网络（其中顶点类型相同），不适用于由多类型顶点及不同语义关系构成的异质信息网络（如书目网络）。本文研究了异质信息网络中高重要性社区搜索问题。我们提出了一种新型社区模型——异质显著社区（HSC），通过多种语义关系揭示具有高属性值的同类型紧密连接顶点。HSC不仅最大化探索锚点类型实体间的间接关系，还整合了其显著性。为搜索HSC，我们首先利用基于分段元路径扩展和显著性增量技术开发在线算法。特别地，我们设计了基于结构嵌套的解空间复用策略以提升效率。此外，我们进一步构建了两级索引以支持HSC的最优时间搜索，并基于此提出了空间高效的紧凑索引。在真实大规模异质信息网络上的广泛实验表明，我们的解决方案能高效搜索HSC，且基于索引的算法比在线算法快2-4个数量级。