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Non-Local Graph Neural Networks
Modern graph neural networks (GNNs) learn node embeddings through multil...
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GAIN: Graph Attention Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs
Graph Neural Networks (GNNs) have led to state-of-the-art performance on...
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Graph Neural Networks with Composite Kernels
Learning on graph structured data has drawn increasing interest in recen...
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Large-Scale Learnable Graph Convolutional Networks
Convolutional neural networks (CNNs) have achieved great success on grid...
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Adaptively Connected Neural Networks
This paper presents a novel adaptively connected neural network (ACNet) ...
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Graph Representation Learning via Hard and Channel-Wise Attention Networks
Attention operators have been widely applied in various fields, includin...
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Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
Noise and inconsistency commonly exist in real-world information network...
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Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Investigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications. Several graph neural network approaches are proposed for node feature learning and they generally follow a neighboring information aggregation scheme to learn node features. While great performance has been achieved, the weights learning for different neighboring nodes is still less explored. In this work, we propose a novel graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores. Experimental results demonstrate the effectiveness of our proposed Node2Seq layer and show that the proposed adaptively non-local information learning can improve the performance of feature learning.
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