Scalable Community Search with Accuracy Guarantee on Attributed Graphs
<|reference_start|>Scalable Community Search with Accuracy Guarantee on Attributed Graphs: 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.<|reference_end|>
@article{wang2024scalable,
    title={Scalable Community Search with Accuracy Guarantee on Attributed Graphs},
    author={Yuxiang Wang, Shuzhan Ye, Xiaoliang Xu, Yuxia Geng, Zhenghe Zhao,
  Xiangyu Ke, Tianxing Wu},
    journal={arXiv preprint arXiv:2402.17242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17242},
    primaryClass={cs.SI cs.DB}
}