Graph convolutional networks

Resource | v1 | created by janarez |
Type Blog post
Created 2016-09-30
Identifier unavailable


Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural network models to such structured datasets. In the last couple of years, a number of papers re-visited this problem of generalizing neural networks to work on arbitrarily structured graphs (Bruna et al., ICLR 2014; Henaff et al., 2015; Duvenaud et al., NIPS 2015; Li et al., ICLR 2016; Defferrard et al., NIPS 2016; Kipf & Welling, ICLR 2017), some of them now achieving very promising results in domains that have previously been dominated by, e.g., kernel-based methods, graph-based regularization techniques and others. In this post, I will give a brief overview of recent developments in this field and point out strengths and drawbacks of various approaches.


about Graph convolutional networks (GCN)

Generalization of neural networks to arbitrary graphs.

reviewed in How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)

This post is about a paper that has just come out recently on practical generalizations of convolutio...

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