AI 中文总结
研究针对重叠社区检测算法在质量与可扩展性平衡上的不足，提出Highway算法，利用网络稀疏骨干进行社区推理。通过与10种算法在728个基准网络上比较，Highway在重叠归一化互信息上领先，其他指标也居前列，实现了准确性与效率的较好权衡。
AI 中文摘要
社区结构在真实网络中很常见，提取它们能为从药物发现到市场细分等应用提供有价值的见解。重叠社区检测（OCD）是对网络数据进行聚类的任务，其中节点可能属于多个簇。现有OCD算法通常难以在检测质量和可扩展性之间取得适当平衡。因此，我们提出了Highway，一种可扩展的OCD算法，它利用输入网络的稀疏骨干进行高效的社区推理。我们使用728个Lancichinetti-Fortunato-Radicchi基准网络，将Highway及其消融版本与10种现有OCD算法进行比较。基于五种性能指标的结果表明，Highway具有竞争力。它在重叠归一化互信息方面排名第一，比最强基线提高了6.9%，在其他四个性能指标中也排名第二。这些比较结果表明，Highway及其骨干过程提供了合适的准确性-效率权衡。Highway算法是开源的，可作为CDlib库的一部分使用。
英文摘要
Community structures are common in real networks, and extracting them provides valuable insight in applications ranging from drug discovery to market segmentation. Overlapping community detection (OCD) is the task of clustering networked data in which nodes may belong to multiple clusters. Existing OCD algorithms often struggle to achieve a suitable balance between detection quality and scalability. We, therefore, propose Highway, a scalable OCD algorithm that exploits the sparse backbone of the input network to perform efficient community inference. We used 728 Lancichinetti-Fortunato-Radicchi benchmark networks to compare Highway and its ablated version against 10 existing OCD algorithms. Our results, based on five performance measures, demonstrate a competitive performance for Highway. It ranks first in overlapping normalized mutual information with a 6.9% improvement over the strongest baseline. It also ranks second in all the other four performance measures. These comparative results suggest that Highway coupled with its backbone procedure offers a suitable accuracy-efficiency trade-off. The Highway algorithm is open-source and available as part of the CDlib library.
CommentsPeer-reviewed and accepted author copy
