Event Details

Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Basically, the current GNNs follow the message-passing framework which receives messages from neighbors and applies neural network to learn node representations. However, previous GNNs mainly focus on homogeneous graph, while in reality, the real-world graphs usually are far from homogeneity. Here we first examine the various types of network heterogeneity, including node and link type heterogeneity,neighborhood heterogeneity, fragment heterogeneity, temporal heterogeneity, and structure heterogeneity. We then discuss the implications and methods to overcome these heterogeneities.

Speaker Bio

Kalpesh is a third year PhD student at UMass Amherst, advised by Prof. Mohit Iyyer. He is primarily interested in natural language generation and the security of NLP systems. Before coming to UMass, he completed a bachelors' degree at IIT Bombay, advised by Prof. Preethi Jyothi. He has also spent time interning at Google, TTI-Chicago and Mozilla. His research is supported by a Google PhD Fellowship, which was awarded in 2021.