Results 11 to 20 of about 9,194 (254)
Exploring the representational power of graph autoencoder [PDF]
Neurocomputing, 2021 While representation learning has yielded a great success on many graph learning tasks, there is little understanding behind the structures that are being captured by these embeddings. For example, we wonder if the topological features, such as the Triangle Count, the Degree of the node and other centrality measures are concretely encoded in the ...
Maroun Haddad, Mohamed Bouguessa
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GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network [PDF]
Frontiers in Genetics, 2021 Wei Lan, Zhixian Liu, Qingfeng Chen
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Interpretable Variational Graph Autoencoder with Noninformative Prior
Future Internet, 2021 Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data.
Lili Sun, Xueyan Liu, Min Zhao, Bo Yang
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Heterogeneous Graph Masked Autoencoders
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph data. However, real-world graphs are always heterogeneous, which poses three critical challenges that existing methods ignore: 1) how to capture complex graph structure? 2) how
Yijun Tian 0001 +4 more
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Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder
Frontiers in Genetics, 2022 The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics.
Yishuai Geng +3 more
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Graph Regularized Variational Ladder Networks for Semi-Supervised Learning
IEEE Access, 2020 To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi ...
Cong Hu, Xiao-Ning Song
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GAE-Based Document Embedding Method for Clustering
IEEE Access, 2022 Document embedding methods for clustering using deep neural networks have been proposed recently. However, the existing deep neural network-based document embedding methods for clustering have a problem of either generating document embeddings dependent ...
Sungwon Jung, Sangmin Ka
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Dirichlet Graph Variational Autoencoder
CoRR, 2020 Inproceedings of NeurIPS ...
Jia Li 0009 +7 more
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Adversarially Regularized Graph Autoencoder for Graph Embedding [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which ...
Shirui Pan +5 more
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Graph Autoencoders with Deconvolutional Networks
CoRR, 2020 Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations. In this paper, we consider their opposite, namely Graph Deconvolutional Networks (GDNs) that reconstruct graph signals from smoothed node representations.
Jia Li 0009 +5 more
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