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Spatial-temporal graph neural network with autoencoder pretraining for intrusion detection in healthcare IoT ecosystems. [PDF]
Sci RepTanvir MIM +5 more
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MCFST: spatial domain identification method based on multi-view graph convolutional network and graph fusion network. [PDF]
BioinformaticsZhang Z, Duan H, Gao X.
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Neural Networks, 2018
In this work, we introduce the graph regularized autoencoder. We propose three variants. The first one is the unsupervised version. The second one is tailored for clustering, by incorporating subspace clustering terms into the autoencoder formulation.
Angshul Majumdar
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In this work, we introduce the graph regularized autoencoder. We propose three variants. The first one is the unsupervised version. The second one is tailored for clustering, by incorporating subspace clustering terms into the autoencoder formulation.
Angshul Majumdar
exaly +4 more sources
Discriminative Graph Autoencoder
2018 IEEE International Conference on Big Knowledge (ICBK), 2018With the abundance of graph-structured data in various applications, graph representation learning has become an effective computational tool for seeking informative vector representations for graphs. Traditional graph kernel approaches are usually frequency-based.
Haifeng Jin, Qingquan Song, Xia Hu
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Context-Aware Graph Convolutional Autoencoder
2021Recommendation problems can be addressed as link prediction tasks in a bipartite graph between user and item nodes, labelled with rating on edges. Existing matrix completion approaches model the user’s opinion on items by ignoring context information that can instead be associated with the edges of the bipartite graph. Context is an important factor to
Asma Sattar, Davide Bacciu
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Scalable Graph Convolutional Variational Autoencoders
2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2021Autoencoders are widely used for self-supervised representation learning. Variational autoencoders (VAEs), a special type of autoencoders, are proven to be effective in estimating the underlying probability distribution of the training data. Even though VAEs are well explored in many application domains, their utilization for graph-structured data is ...
Unyi, Dániel, Gyires-Tóth, Bálint
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RARE: Robust Masked Graph Autoencoder
IEEE Transactions on Knowledge and Data EngineeringZhiping Cai, Chuan Ma, Zhe Liu
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A Simple Training Strategy for Graph Autoencoder
Proceedings of the 2020 12th International Conference on Machine Learning and Computing, 2020Graph autoencoder can map graph data into a low-dimensional space. It is a powerful graph embedding method applied in graph analytics to reduce the computational cost. The training algorithm of a graph autoencoder searches the weight setting for preserving most graph information of the graph data with reduced dimensionality.
Yingfeng Wang +3 more
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Graph Autoencoder Combined with Attribute Information in Graph
2020 6th International Conference on Big Data and Information Analytics (BigDIA), 2020Network embedding is an important method to learn the low-dimensional representation of nodes. It can be used to reduce the dimension of the network for downstream tasks. Many existing embeddings learn the representation only based on the reconstruction of the topological structure, yet nodes with attributes can provide important information in many ...
Xianchen Zhou +3 more
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