大规模网络中的核心-外围识别
Core-periphery identification in massive networks
- Akita International University（秋田国际大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对大规模网络核心-外围结构识别需求，提出基于边列表表示的分治算法，在含1400万条边的真实网络上验证了性能，无需全量加载网络即可高效识别结构。
AI中文摘要：
现代网络规模庞大，节点和边可达数百万甚至数十亿个，因此算法必须具备可扩展性，才能在实际应用中发挥作用。本研究致力于开发一种适用于大规模网络的核心-外围结构识别算法。核心-外围结构是一种中尺度特征，节点被划分为连接紧密的核心或连接稀疏的外围。为在大型网络中识别此类结构，我们提出一种分治算法，其关键特性是利用网络的边列表表示而非邻接矩阵，这一方式运行速度更快且内存使用效率更高。我们将该算法应用于合成数据和真实世界数据，尤其在几乎含1400万条边的真实网络上验证了其性能，且无需将整个网络加载至内存中。
英文摘要：
Modern networks can be huge with millions or even billions of nodes and edges. Thus, algorithms must be capable of scaling to such large networks in order to be practically useful. In this work, we are interested in developing an algorithm to identify core-periphery structure in massive networks. Core-periphery structure is a meso-scale feature where nodes are grouped into a densely connected core or sparsely connected periphery. To identify such structures in large networks, we propose a divide-and-conquer algorithm. The key feature of our algorithm is leveraging the edge list representation of the network, instead of the adjacency matrix, as it tends to be faster and makes a more efficient use of memory. We apply the proposed algorithm to synthetic and real-world data, notably demonstrating its performance on a real-world network with almost 14 million edges without loading the entire network into memory.
