Knowledge-Guided Graph Representation Learning
Graph-structured data are ubiquitous, which have been extensively used in many domains such as social networks and recommender systems. In this talk, I will present our recent work on graph representation learning with applications in multiple domains. First, we leverage the line graph theory and propose novel graph neural networks, which jointly learn embeddings for both nodes and edges. Second, we investigate how to incorporate commonsense and domain knowledge to graph representation learning, and present several applications in natural language processing, computer vision, and recommender systems. Finally, future work on knowledge-guided graph representation learning will also be discussed.