Graph Attention Networks Under the Hood

Graph Attention Networks Under the Hood

A Step-by-step Guide From Math to NumPy

Graph Neural Networks (GNNs) have emerged as the standard toolbox to learn from graph data. GNNs are able to drive improvements for high-impact problems in different fields, such as content recommendation or drug discovery. Unlike other types of data such as images, learning from graph data requires specific methods. As defined by 

Michael Bronstein

:[..] these methods are based on some form of message passing on the graph allowing different nodes to exchange information.

For accomplishing specific tasks on graphs (node classification, link prediction, etc.), a GNN layer computes the node and the edge representations through the so-called recursive neighborhood diffusion (or message passing). According to this principle, each graph node receives and aggregates features from its neighbors in order to represent the local graph structure: different types of GNN layers perform diverse aggregation strategies.

graph-attention-networks under-the-hood gnns-series numpy

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How to Explain Graph Neural Network — GNNExplainer

Unlike CNN, where we can extract activation of each layer to visualize the decisions of the network, in GNN it is hard to get a meaningful explanation of what features the network has learnt. Here's Step-by-step guide for a GNNExplainer for node and graph explanation implemented in PyTorch Geometric.