Graph Neural Networks: Benchmarks and Future Directions

Abstract

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this talk, I will introduce our open-source benchmarking framework to identify scalable and powerful GNN architectures, and track the progress of graph representation learning.

Chaitanya K. Joshi

PhD Student