Graph neural network: Difference between revisions

Revision as of 21:27, 10 June 2022

If you can represent your data as a graph, you can use a graph neural network to perform inference on the data and output a new graph or predictions on the graph.

Introduction

Structure

A graph neural network consists of layers which operate on graphs.

At each layer, you get the following features during inference:

The graph structure
Node embeddings
Edge embeddings
Graph embeddings

Message Passing Layer

A standard GNN layer consists of pooling functions followed by update functions.

Pooling

There are many types of pooling to choose from:

For nodes, you can add the node embedding to connected node embedding or connected edge embeddings.
Similarly for edges, you can add its embedding to connected node embeddings.
For the entire graph, you can add all node embeddings together.

After pooling, you can use update function (e.g. MLP) to update the embeddings/state at each node, edge, and for the entire graph.

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+If you can represent your data as a graph, you can use a graph neural network to perform inference on the data and output a new graph or predictions on the graph.
+==Introduction==
+===Structure===
+A graph neural network consists of layers which operate on graphs.
+At each layer, you get the following features during inference:
+* The graph structure
+* Node embeddings
+* Edge embeddings
+* Graph embeddings
+===Message Passing Layer===
+A standard GNN layer consists of pooling functions followed by update functions.
+===Pooling===
+There are many types of pooling to choose from:
+* For nodes, you can add the node embedding to connected node embedding or connected edge embeddings.
+* Similarly for edges, you can add its embedding to connected node embeddings.
+* For the entire graph, you can add all node embeddings together.
+After pooling, you can use update function (e.g. MLP) to update the embeddings/state at each node, edge, and for the entire graph.
 ==Resources==
 * [https://distill.pub/2021/gnn-intro/ A Gentle Introduction to Graph Neural Networks (2021) by Google Research]
 * [https://distill.pub/2021/understanding-gnns/ Understanding Convolutions on Graphs (2021) by Google Research]