Graph Convolutional Networks – Intro to neural networks on graphs
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...
Graph Convolutional Networks – Intro to neural networks on graphs launched on October 1, 2016 with 106 votes, #348 of 3,690 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 10 years on.
In plain words
Graph Convolutional Networks is an educational resource introducing neural network techniques for graph-structured data. It explains how to apply neural networks to datasets like social networks, knowledge graphs, and protein-interaction networks, which have traditionally been difficult to process with standard deep learning approaches. The resource covers multi-layer Graph Convolutional Networks using first-order filters, making it suitable for researchers and developers working with networked or relational data.
written from the facts on this page · September 2026
From the sources
Multi-layer Graph Convolutional Network (GCN) with first-order filters. 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…from tkipf.github.io
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