Jian Tang

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Towards Combining Statistical Relational Learning and Graph Neural Networks

Developing statistical machine learning methods for predictions on graphs has been a fundamental problem for many applications such as semi-supervised node classification and link prediction on knowledge graphs. Such problems have been extensively studied by traditional statistical relational learning methods and recent graph neural networks, which are attracting increasing attention. In this talk, I will introduce our recent efforts to combine the advantages of both worlds for prediction and reasoning on graphs. I will introduce our work on combining conditional random fields and graph neural networks for semi-supervised node classification (Graph Markov Neural Networks, ICML'19) and also recent work on combining Markov Logic Networks and knowledge graph embedding (Probabilistic Logic Neural Network, in submission) for reasoning on knowledge graphs.

Dr. Jian Tang is an assistant professor at Mila (Quebec AI institute) and HEC Montreal since December, 2017. He is named to the first cohort of Canada CIFAR Artificial Intelligence Chairs (CIFAR AI Research Chair). His research interests focus on deep graph representation learning with a variety of applications such as knowledge graphs, drug discovery and recommender systems. He was a research fellow at the University of Michigan and Carnegie Mellon University. He received his Ph.D degree from Peking University and was a visiting student at the University of Michigan for two years. He was a researcher in Microsoft Research Asia for two years. His work on graph representation learning (e.g., LINE, LargeVis, and RotatE) are widely recognized. He received the best paper award of ICML’14 and was nominated for the best paper of WWW’16.

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