You are all cordially invited to the AMLab seminar on **Tuesday December 13 at 16:00 in C3.163**, where **Thomas Kipf** will give a talk titled “**Deep Learning on Graphs with Graph Convolutional Networks**”. Afterwards there are the usual drinks and snacks!

**Abstract:** Deep learning has recently enabled breakthroughs in the fields of computer vision and natural language processing. Little attention, however, has been devoted to the generalization of deep neural network-based models to datasets that come in the form of graphs or networks (e.g. social networks, knowledge graphs or protein-interaction networks). Generalizing convolutional neural networks, the workhorse of deep learning, to graph-structured data is not straightforward and a number of different approaches have been introduced (see [1] for an overview). I will review some of these models and introduce our own variant of graph convolutional networks [2] that achieves competitive performance on a number of semi-supervised node classification tasks. I will further talk about extensions to the basic graph convolutional framework, with special focus on our recently introduced variational graph auto-encoder [3]—a model for unsupervised learning and link prediction—and outline future research directions.

[1] Graph Convolutional Networks, http://tkipf.github.io/graph-convolutional-networks/

[2] TN Kipf and M Welling, Semi-Supervised Classification with Graph Convolutional Networks, arXiv:1609.02907, 2016

[3] TN Kipf and M Welling, Variational Graph Auto-Encoders, NIPS Bayesian Deep Learning Workshop, 2016