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World Wide Web
Erschienen in: World Wide Web 2/2020

27.11.2019

Gaussian mixture embedding of multiple node roles in networks

verfasst von: Yujun Chen, Juhua Pu, Xingwu Liu, Xiangliang Zhang

Erschienen in: World Wide Web | Ausgabe 2/2020

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Abstract

Network embedding is a classical topic in network analysis. Current network embedding methods mostly focus on deterministic embedding, which maps each node as a low-dimensional vector. Thus, the network uncertainty and the possible multiple roles of nodes cannot be well expressed. In this paper, we propose to embed a single node as a mixture of Gaussian distribution in a low-dimensional space. Each Gaussian component corresponds to a latent role that the node plays. The proposed approach thus can characterize network nodes in a comprehensive representation, especially bridging nodes, which are relevant to different communities. Experiments on real-world network benchmarks demonstrate the effectiveness of our approach, outperforming the state-of-the-art network embedding methods. Also, we demonstrate that the number of components learned for each node is highly related to its topology features, such as node degree, centrality and clustering coefficient.
Vorheriger Artikel Distributed and parallel processing for real-time and dynamic spatio-temporal graph
Nächster Artikel Editorial: Special issue on security and privacy in network computing

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Metadaten
Titel
Gaussian mixture embedding of multiple node roles in networks
verfasst von
Yujun Chen
Juhua Pu
Xingwu Liu
Xiangliang Zhang
Publikationsdatum
27.11.2019
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 2/2020
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-019-00743-4

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