Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning

Explore subgraph-based networks for graph learning in this IEEE Signal Processing Society webinar presented by Haggai Maron from NVIDIA Research. Delve into the concepts of explicit power, color refinement, and exclusivity in graph learning. Understand the importance of equivariance and its benefits in neural networks. Examine the DSSA architecture, its theoretical and experimental analysis, and its application to large graphs. Learn about node-based policies, tensors, and other approaches in graph learning. Gain insights into the Sun architecture and its experimental results. Conclude with a summary and Q&A session on expressive, efficient, and domain-independent graph learning techniques.