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Topology Adaptive Graph Convolutional Networks
Convolution acts as a local feature extractor in convolutional neural ne...
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DAGCN: Dual Attention Graph Convolutional Networks
Graph convolutional networks (GCNs) have recently become one of the most...
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How to learn a graph from smooth signals
We propose a framework that learns the graph structure underlying a set ...
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Graph Embedding for Recommendation against Attribute Inference Attacks
In recent years, recommender systems play a pivotal role in helping user...
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An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
This paper introduces a new learning-based approach for approximately so...
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Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
This paper reviews graph convolutional neural networks (GCNNs) through t...
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LanczosNet: Multi-Scale Deep Graph Convolutional Networks
We propose the Lanczos network (LanczosNet), which uses the Lanczos algo...
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Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.
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