网络中最大Flexi-Clique的高效计算
Efficient Computation of Maximum Flexi-Clique in Networks
AI总结：
本文提出Flexi-clique模型，通过亚线性度数约束克服传统模型的固定阈值限制，并设计FPA和EBA算法在大规模网络中高效发现大规模紧密子图。
摘要：
发现大规模紧密子图是图挖掘中的关键任务。现有模型，如clique、k-plex和γ-quasi-clique，使用固定的密度阈值，忽略了随着子图规模增加而自然衰减的连接性。Flexi-clique模型通过施加一个随着子图规模增长而呈亚线性增长的度数约束来克服这一限制。我们对Flexi-clique进行了算法研究，证明了其NP难性，并分析了其非传承性。为解决其计算挑战，我们提出Flexi-Prune算法FPA，一种使用核心基于播种和连接性感知修剪的快速启发式算法，以及Efficient Branch-and-Bound算法EBA，一种通过多种修剪规则增强的精确框架。在大规模真实世界和合成网络上的实验表明，FPA在成本显著降低的情况下实现了接近最优的质量，而EBA能够高效地计算精确解。因此，Flexi-clique为在复杂网络中发现大规模、有意义的子图提供了实用且可扩展的模型。
英文摘要：
Discovering large cohesive subgraphs is a key task for graph mining. Existing models, such as clique, k-plex, and γ-quasi-clique, use fixed density thresholds that overlook the natural decay of connectivity as the subgraph size increases. The Flexi-clique model overcomes this limitation by imposing a degree constraint that grows sub-linearly with subgraph size. We provide the algorithmic study of Flexi-clique, proving its NP-hardness and analysing its non-hereditary properties. To address its computational challenge, we propose the Flexi-Prune Algorithm FPA, a fast heuristic using core-based seeding and connectivity-aware pruning, and the Efficient Branch-and-Bound Algorithm EBA, an exact framework enhanced with multiple pruning rules. Experiments on large real-world and synthetic networks demonstrate that FPA achieves near-optimal quality at much lower cost, while EBA efficiently computes exact solutions. Flexi-clique thus provides a practical and scalable model for discovering large, meaningful subgraphs in complex networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
