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When Kernel Fusion meets Graph Neural Networks

This blog describes fused message passing, the key technique enabling these performance improvements. We will address the following questions. (1) Why cannot basic message passing scale to large graphs? (2) How does fused message passing help? (3) How to enable fused message passing in DGL?

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Understand Graph Attention Network

From Graph Convolutional Network (GCN), we learned that combining local graph structure and node-level features yields good performance on node classification task. However, the way GCN aggregates is structure-dependent, which may hurt its generalizability. Graph Attention Network proposes an alternative way by weighting neighbor features with feature dependent and structure free normalization, in the style of attention. The goal of this tutorial: (1) Explain what is Graph Attention Network. (2) Demonstrate how it can be implemented in DGL. (3) Understand the attentions learnt. (4) Introduce to inductive learning.

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Batched Graph Classification with DGL

Graph classification is an important problem with applications across many fields -- bioinformatics, chemoinformatics, social network analysis, urban computing and cyber-security. Applying graph neural networks to this problem has been a popular approach recently. This tutorial is a demonstration for: (1) batching multiple graphs of variable size and shape with DGL (2) training a graph neural network for a simple graph classification task

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