Tag: Graph Representation Learning
All the talks with the tag "Graph Representation Learning".
The Expressive Power of GNNs
Pankaj KumarPublished: at 02:00 PMThis talk delves into the theoretical foundations of Graph Neural Networks (GNNs), focusing on their ability to model complex relationships between node features and graph structures. While GNNs excel in tasks like node classification and link prediction, their expressive power remains challenging to quantify due to the interplay of graph topology and node features. The discussion highlights key challenges, such as trade-offs between expressiveness and computational efficiency, and explores advancements like the connection between GNNs and the Weisfeiler-Lehman graph isomorphism test. The talk also outlines future directions for developing rigorous evaluation methods to better understand and assess GNN expressivity.
Class-Imbalanced Learning on Graphs - A Survey
Rishav DasPublished: at 02:00 PMThis survey provides a comprehensive understanding of class-imbalanced learning on graphs (CILG), a promising solution that combines graph representation learning and class-imbalanced learning. It presents a taxonomy of existing work, analyzes recent advancements, and discusses future research directions in CILG.
Handling Missing Data with Graph Representation Learning
Rucha Bhalchandra JoshiPublished: at 07:00 PMIn this talk, we will look at a paper that proposes a graph-based framework for feature imputation as well as label prediction, called GRAPE.