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  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • S -S C GRAPH CONVOLUTIONAL NETWORKS - OpenReview
    ABSTRACT We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs We motivate the choice of our convolutional archi-tecture via a localized first-order approximation of spectral graph convolutions Our model scales linearly in the number of graph edges and learns
  • FastGCN: Fast Learning with Graph Convolutional Networks via. . .
    The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning Such a model, however, is transductive in nature because parameters are learned through convolutions with both training and test data
  • Semi-Supervised Classification with Graph Convolutional Networks . . .
    TL;DR: Semi-supervised classification with a CNN model for graphs State-of-the-art results on a number of citation network datasets Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs
  • Topology Adaptive Graph Convolutional Networks - OpenReview
    In NIPS2016 Graph Convolutional Neural Networks with Complex Rational Spectral Filters, submitted to ICLR18 Kipf, T N , Welling, M Semi-supervised classification with graph convolutional networks In ICLR2017
  • Graph Partition Neural Networks for Semi-Supervised Classification
    We extensively test our model on a variety of semi-supervised node classification tasks Experimental results indicate that GPNNs are either superior or comparable to state-of-the-art methods on a wide variety of datasets for graph-based semi-supervised classification
  • TWIN GRAPH CONVOLUTIONAL NETWORKS: GCN WITH DUAL GRAPH SUPPORT FOR SEMI . . .
    Keywords: Graph, Neural Networks, Deep Learning, semi-supervised learning TL;DR: A primal dual graph neural network model for semi-supervised learning Abstract: Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains
  • ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks . . .
    Abstract Compared to sequential learning models, graph-based neural networks exhibit excel-lent ability in capturing global information and have been used for semi-supervised learn-ing tasks, including citation network analysis or text classification However, most GCNs are designed with the single-dimensional edge feature and neglected to utilise the rich edge information about graphs In
  • Graph Attention Networks - OpenReview
    A novel approach to processing graph-structured data by neural networks, leveraging attention over a node's neighborhood Achieves state-of-the-art results on transductive citation network tasks and an inductive protein-protein interaction task
  • Relational Graph Attention Networks - OpenReview
    Keywords: RGCN, attention, graph convolutional networks, semi-supervised learning, graph classification, molecules TL;DR: We propose a new model for relational graphs and evaluate it on relational transductive and inductive tasks





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