Consensus embedding for multiple networks: Computation and applications

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Mengzhen Li, Mustafa Coşkun, Mehmet Koyutürk
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引用次数: 1

Abstract

Abstract Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a low-dimensional space such that the nodes that are “similar” with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be “multiplex” with multiple types of edges with different semantics. For such networks, computation of Consensus Embeddings based on the node embeddings of individual versions can be useful for various reasons, including privacy, efficiency, and effectiveness of analyses. Here, we systematically investigate the performance of three dimensionality reduction methods in computing consensus embeddings on networks with multiple versions: singular value decomposition, variational auto-encoders, and canonical correlation analysis (CCA). Our results show that (i) CCA outperforms other dimensionality reduction methods in computing concensus embeddings, (ii) in the context of link prediction, consensus embeddings can be used to make predictions with accuracy close to that provided by embeddings of integrated networks, and (iii) consensus embeddings can be used to improve the efficiency of combinatorial link prediction queries on multiple networks by multiple orders of magnitude.
多网络共识嵌入:计算与应用
摘要大规模网络结构化数据上的机器学习应用通常以节点嵌入的形式对网络信息进行编码。网络嵌入算法将节点映射到低维空间中,使得相对于网络拓扑“相似”的节点在嵌入空间中也彼此接近。现实世界的网络通常有多个版本,或者可以是具有不同语义的多种类型边缘的“多路复用”网络。对于这样的网络,基于各个版本的节点嵌入的共识嵌入的计算可能由于各种原因而有用,包括分析的隐私性、效率和有效性。在这里,我们系统地研究了在具有多个版本的网络上计算一致嵌入的三维降维方法的性能:奇异值分解、变分自动编码器和规范相关分析(CCA)。我们的结果表明,(i)CCA在计算一致性嵌入方面优于其他降维方法,(ii)在链路预测的背景下,一致性嵌入可以用于进行精度接近集成网络嵌入的预测,以及(iii)一致性嵌入可以用于将多个网络上的组合链路预测查询的效率提高多个数量级。
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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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