Dual-Primal Graph Convolutional Networks

by   Federico Monti, et al.

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.


Topology Adaptive Graph Convolutional Networks

Convolution acts as a local feature extractor in convolutional neural ne...

DAGCN: Dual Attention Graph Convolutional Networks

Graph convolutional networks (GCNs) have recently become one of the most...

Simple Graph Convolutional Networks

Many neural networks for graphs are based on the graph convolution opera...

How to learn a graph from smooth signals

We propose a framework that learns the graph structure underlying a set ...

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

This paper introduces a new learning-based approach for approximately so...

Directed Graph Convolutional Network

Graph Convolutional Networks (GCNs) have been widely used due to their o...

Quantifying the alignment of graph and features in deep learning

We show that the classification performance of Graph Convolutional Netwo...