论文标题
基于二次优化的集团扩展，以重叠社区检测
Quadratic Optimization based Clique Expansion for Overlapping Community Detection
论文作者
论文摘要
社区发现对于分析社会和生物网络至关重要，在过去的二十年中，已经提出了全面的方法。然而，在可以准确近似地面真相社区的大型网络中找到所有重叠的社区仍然具有挑战性。在这项工作中，我们介绍了QOCE（基于二次优化的集合扩展），这是一种重叠的社区检测算法，可以扩展到具有数十万节点和数百万个边缘的大型网络。 Qoce遵循流行的种子集扩展策略，将每个高质量的最大列集作为初始种子集，并为扩展应用二次优化。我们对28个合成LFR网络和各种域和尺度的六个现实世界网络进行了广泛评估我们的算法，并将QOCE与四个最新的重叠社区检测算法进行比较。经验结果证明了拟议方法在检测准确性，效率和可伸缩性方面的竞争性能。
Community detection is crucial for analyzing social and biological networks, and comprehensive approaches have been proposed in the last two decades. Nevertheless, finding all overlapping communities in large networks that could accurately approximate the ground-truth communities remains challenging. In this work, we present the QOCE (Quadratic Optimization based Clique Expansion), an overlapping community detection algorithm that could scale to large networks with hundreds of thousands of nodes and millions of edges. QOCE follows the popular seed set expansion strategy, regarding each high-quality maximal clique as the initial seed set and applying quadratic optimization for the expansion. We extensively evaluate our algorithm on 28 synthetic LFR networks and six real-world networks of various domains and scales, and compare QOCE with four state-of-the-art overlapping community detection algorithms. Empirical results demonstrate the competitive performance of the proposed approach in terms of detection accuracy, efficiency, and scalability.
