Title:
An Overview of Graph Neural Network Propagation Modules
Abstract:
Graph Neural Networks (GNNs) offer powerful methods to analyze the complex relationships inherent in graph-structured data. Propagation modules sit at the core of GNNs, driving the process of learning representations by aggregating information across nodes. This talk explores the landscape of GNN propagation modules. We’ll delve into the fundamentals of spectral and spatial approaches, examining techniques like ChebNet, GCN, AGCN, and DGCN. If time allows, we’ll also investigate recurrent propagation, sampling, and the importance of skip connections within GNN architectures. Finally, we’ll spotlight the leading frameworks for GNN development: Deep Graph Library (DGL) and PyTorch Geometric.
Graph Neural Networks (GNNs) offer powerful methods to analyze the complex relationships inherent in graph-structured data. Propagation modules sit at the core of GNNs, driving the process of learning representations by aggregating information across nodes. This talk explores the landscape of GNN propagation modules. We’ll delve into the fundamentals of spectral and spatial approaches, examining techniques like ChebNet, GCN, AGCN, and DGCN. If time allows, we’ll also investigate recurrent propagation, sampling, and the importance of skip connections within GNN architectures. Finally, we’ll spotlight the leading frameworks for GNN development: Deep Graph Library (DGL) and PyTorch Geometric.