Graph convolutional networks

Resource history | v1 (current) | created by janarez on Apr 1, 2021

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Title: Graph convolutional networks
Type: BlogPost
Created: 2016-09-30
Description: 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.
Link: http://tkipf.github.io/graph-convolutional-networks/
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