属性图上具有精度保证的可扩展社区搜索
Scalable Community Search with Accuracy Guarantee on Attributed Graphs
AI总结：
针对属性图社区搜索效率、精度保证及查询相关性问题，提出无索引采样估计方法，支持多种图与社区模型，并在十个真实数据集上显著加速且保证误差。
摘要：
给定一个属性图 $G$ 和一个查询节点 $q$，属性图上的社区搜索（Community Search over Attributed Graphs，CS-AG）旨在从 $G$ 中找到一个包含 $q$、在结构和属性上均具有内聚性的子图。尽管 CS-AG 已得到广泛研究，但仍面临三个挑战：（1）基于图遍历的精确方法非常耗时，尤其在大图上；一些定制索引可以提升效率，却会带来不可忽略的存储和维护开销。（2）近似比宽松的近似方法只能对社区质量进行粗粒度评估，而无法在运行时提供具有精度保证的可靠评估。（3）属性内聚性度量往往忽略了与查询节点 $q$ 之间的重要相关性。我们在同构图的 $k$-core 模型上，基于一种同时考虑文本和数值属性、以 $q$ 为中心的属性内聚性度量，形式化定义了 CS-AG 问题，并证明该问题是 NP-hard。为求解该问题，我们首先提出一个带有三种剪枝策略的精确基线；随后提出一种无索引、基于采样估计的方法，以置信区间形式快速返回具有精度保证的近似社区。一旦获得满足用户期望误差界的良好结果，就提前终止。我们还将其扩展到异构图、$k$-truss 模型以及规模受限的 CS。在十个真实数据集上的综合实验表明了该方法的优越性，例如响应时间至少快 1.54 倍（平均快 41.1 倍），并且属性内聚性实现了可靠的相对误差（处于用户指定误差界内）。
英文摘要：
Given an attributed graph $G$ and a query node $q$, \underline{C}ommunity \underline{S}earch over \underline{A}ttributed \underline{G}raphs (CS-AG) aims to find a structure- and attribute-cohesive subgraph from $G$ that contains $q$. Although CS-AG has been widely studied, they still face three challenges. (1) Exact methods based on graph traversal are time-consuming, especially for large graphs. Some tailored indices can improve efficiency, but introduce nonnegligible storage and maintenance overhead. (2) Approximate methods with a loose approximation ratio only provide a coarse-grained evaluation of a community's quality, rather than a reliable evaluation with an accuracy guarantee in runtime. (3) Attribute cohesiveness metrics often ignores the important correlation with the query node $q$. We formally define our CS-AG problem atop a $q$-centric attribute cohesiveness metric considering both textual and numerical attributes, for $k$-core model on homogeneous graphs. We show the problem is NP-hard. To solve it, we first propose an exact baseline with three pruning strategies. Then, we propose an index-free sampling-estimation-based method to quickly return an approximate community with an accuracy guarantee, in the form of a confidence interval. Once a good result satisfying a user-desired error bound is reached, we terminate it early. We extend it to heterogeneous graphs, $k$-truss model, and size-bounded CS. Comprehensive experimental studies on ten real-world datasets show its superiority, e.g., at least 1.54$\times$ (41.1$\times$ on average) faster in response time and a reliable relative error (within a user-specific error bound) of attribute cohesiveness is achieved.
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