期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 6, 页码 2475-2487出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2932096
关键词
Task analysis; Training; Clustering algorithms; Generators; Convolutional codes; Decoding; Data models; Adversarial regularization; graph autoencoder; graph clustering; graph convolutional networks (GCNs); graph embedding; link prediction
类别
资金
- Australian Government through the Australian Research Council
- Australia Government Department of Health [LP160100630]
- Australia Research Alliance for Children and Youth and Global Business College Australia [LP150100671]
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding distribution of the latent codes, which unfortunately may lead to inferior representation in many cases. In this article, we present a novel adversarially regularized framework for graph embedding. By employing the graph convolutional network as an encoder, our framework embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph. The adversarial training principle is applied to enforce our latent codes to match a prior Gaussian or uniform distribution. Based on this framework, we derive two variants of the adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, and adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit other potential variations of ARGA and ARVGA to get a deeper understanding of our designs. Experimental results that compared 12 algorithms for link prediction and 20 algorithms for graph clustering validate our solutions.
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