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

Resource | v1 | created by janarez |
Type Blog post
Created 2016-09-13
Identifier unavailable


This post is about a paper that has just come out recently on practical generalizations of convolutional layers to graphs: Thomas N. Kipf and Max Welling (2016) Semi-Supervised Classification with Graph Convolutional Networks Along the way I found this earlier, related paper: Defferrard, Bresson and Vandergheynst (NIPS 2016) Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering This post is mainly a review of (Kipf and Welling, 2016). The paper is nice to read, and while I like the general idea, I feel like the approximations made in the paper are too limiting and severely hurt the generality of the models we can build. This post explains why.


gives cons of Graph convolutional networks (GCN)

Generalization of neural networks to arbitrary graphs.

reviews Graph convolutional networks

Many important real-world datasets come in the form of graphs or networks: social networks, knowledge...

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