AOC: Assembling overlapping communities

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE Quantitative Science Studies Pub Date : 2022-08-05 DOI:10.1162/qss_a_00227
Akhil Jakatdar, T. Warnow, George Chacko
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引用次数: 2

Abstract

Abstract Through discovery of mesoscale structures, community detection methods contribute to the understanding of complex networks. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities because some nodes may reasonably be assigned to multiple communities. We have previously reported Iterative K-core Clustering, a scalable and modular pipeline that discovers disjoint research communities from the scientific literature. We now present Assembling Overlapping Clusters (AOC), a complementary metamethod for overlapping communities, as an option that addresses the disjoint clustering problem. We present findings from the use of AOC on a network of over 13 million nodes that captures recent research in the very rapidly growing field of extracellular vesicles in biology.
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AOC:组装重叠的社区
通过发现中尺度结构,群落检测方法有助于理解复杂网络。然而,许多社区查找方法依赖于不相交聚类技术,其中节点成员仅限于一个社区或集群。这种严格的要求限制了包容性描述社区的能力,因为一些节点可能被合理地分配给多个社区。我们之前报道过迭代K-core集群,这是一个可扩展的模块化管道,可以从科学文献中发现不一致的研究社区。我们现在提出装配重叠集群(AOC),这是重叠社区的一种补充元方法,作为解决不相交聚类问题的一种选择。我们展示了在超过1300万个节点的网络上使用AOC的发现,该网络捕获了生物学中非常快速发展的细胞外囊泡领域的最新研究。
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来源期刊
Quantitative Science Studies
Quantitative Science Studies INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
12.10
自引率
12.50%
发文量
46
审稿时长
22 weeks
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