Searching and detecting communities in real-world graphs underpins a wide range of applications. Despite the success achieved, current learning-based solutions regard community search, i.e., locating the best community for a given query, and community detection, i.e., partitioning the whole graph, as separate problems, necessitating task- and dataset-specific retraining. Such a strategy limits the applicability and generalization ability of the existing models. Additionally, these methods rely heavily on information from the target dataset, leading to suboptimal performance when supervision is limited or unavailable. To mitigate this limitation, we propose UniCom, a unified framework to solve both community search and detection tasks through knowledge transfer across multiple domains, thus alleviating the limitations of single-dataset learning. UniCom centers on a Domain-aware Specialization (DAS) procedure that adapts on the fly to unseen graphs or tasks, eliminating costly retraining while maintaining framework compactness with a lightweight prompt-based paradigm. This is empowered by a Universal Graph Learning (UGL) backbone, which distills transferable semantic and topological knowledge from multiple source domains via comprehensive pre-training. Both DAS and UGL are informed by local neighborhood signals and cohesive subgraph structures, providing consistent guidance throughout the framework. Extensive experiments on both tasks across 16 benchmark datasets and 22 baselines have been conducted to ensure a comprehensive and fair evaluation. UniCom consistently outperforms all state-of-the-art baselines across all tasks under settings with scarce or no supervision, while maintaining runtime efficiency.
Community Search in Time-dependent Road-social Attributed Networks
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Skyline Community Search over Edge-Attributed Bipartite Graphs
State Key Lab of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；University of Chinese Academy of Sciences（中国科学院大学）
Advancing Community Detection with Graph Convolutional Neural Networks: Bridging Topological and Attributive Cohesion
Victoria University of Wellington（维多利亚大学）；Goldenset Collective（Goldenset集体）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
WCCS: Efficient Wedge Conductance Community Search over Large Temporal Bipartite Graphs (Full Paper)
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；Huazhong University of Science and Technology（华中科技大学）；Beijing Institute of Technology（北京理工大学）
