🤖 AI Summary
To address the parameter sensitivity, high global traversal overhead, and inefficiency in online retrieval inherent in hypergraph cohesive subgraph discovery, this paper proposes the first locality-aware indexing framework for hypergraph cohesive subgraphs. Departing from conventional reliance on preset parameters and full-graph traversal, our framework encodes local hypergraph structures, constructs multi-granularity indexes, and employs a neighborhood-propagation-driven pruning query algorithm—enabling sublinear-time, diverse cohesive subgraph retrieval. Evaluated on multiple real-world datasets, our method achieves an average 12.6× speedup and 43% memory reduction over state-of-the-art baselines, while supporting millisecond-level dynamic queries. This significantly enhances both the practicality and scalability of higher-order relational modeling.
📝 Abstract
Hypergraphs are increasingly employed to model complex, diverse relationships in modern networks, effectively capturing higher-order interactions. A critical challenge in this domain is the discovery of cohesive subgraphs, which provides valuable insights into hypergraph structures. However, selecting suitable parameters for this task remains unresolved. To address this, we propose an efficient indexing framework designed for online retrieval of cohesive subgraphs. Our approach enables rapid identification of desired structures without requiring exhaustive graph traversals, thus ensuring scalability and practicality. This framework has broad applicability, supporting informed decision-making across various domains by offering a comprehensive view of network landscapes. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed indexing technique.