book

Hands-On Graph Neural Networks Using Python

Name: Hands-On Graph Neural Networks Using Python
Author: Maxime Labonne
ISBN: 9781804617526

by Maxime Labonne

April 2023

Intermediate to advanced

354 pages

8h 22m

English

Packt Publishing

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Hands-On Graph Neural Networks Using Python
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare your thoughtsDownload a free PDF copy of this book
Part 1: Introduction to Graph Learning
Chapter 1: Getting Started with Graph Learning
Why graphs?Why graph learning?Why graph neural networks?SummaryFurther reading
Chapter 2: Graph Theory for Graph Neural Networks
Technical requirementsIntroducing graph propertiesDirected graphsWeighted graphsConnected graphsTypes of graphsDiscovering graph conceptsFundamental objectsGraph measuresAdjacency matrix representationExploring graph algorithmsBreadth-first searchDepth-first searchSummary
Chapter 3: Creating Node Representations with DeepWalk
Technical requirementsIntroducing Word2VecCBOW versus skip-gramCreating skip-gramsThe skip-gram modelDeepWalk and random walksImplementing DeepWalkSummaryFurther reading
Part 2: Fundamentals
Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec
Technical requirementsIntroducing Node2VecDefining a neighborhoodIntroducing biases in random walksImplementing Node2VecBuilding a movie RecSysSummaryFurther reading
Chapter 5: Including Node Features with Vanilla Neural Networks
Technical requirementsIntroducing graph datasetsThe Cora datasetThe Facebook Page-Page datasetClassifying nodes with vanilla neural networksClassifying nodes with vanilla graph neural networksSummaryFurther reading
Chapter 6: Introducing Graph Convolutional Networks
Technical requirementsDesigning the graph convolutional layerComparing graph convolutional and graph linear layersPredicting web traffic with node regressionSummaryFurther reading

Chapter 7: Graph Attention Networks
Technical requirementsIntroducing the graph attention layerLinear transformationActivation functionSoftmax normalizationMulti-head attentionImproved graph attention layerImplementing the graph attention layer in NumPyImplementing a GAT in PyTorch GeometricSummary
Part 3: Advanced Techniques
Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE
Technical requirementsIntroducing GraphSAGENeighbor samplingAggregationClassifying nodes on PubMedInductive learning on protein-protein interactionsSummaryFurther reading
Chapter 9: Defining Expressiveness for Graph Classification
Technical requirementsDefining expressivenessIntroducing the GINClassifying graphs using GINGraph classificationImplementing the GINSummaryFurther reading
Chapter 10: Predicting Links with Graph Neural Networks
Technical requirementsPredicting links with traditional methodsHeuristic techniquesMatrix factorizationPredicting links with node embeddingsIntroducing Graph AutoencodersIntroducing VGAEsImplementing a VGAEPredicting links with SEALIntroducing the SEAL frameworkImplementing the SEAL frameworkSummaryFurther reading
Chapter 11: Generating Graphs Using Graph Neural Networks
Technical requirementsGenerating graphs with traditional techniquesThe Erdős–Rényi modelThe small-world modelGenerating graphs with graph neural networksGraph variational autoencodersAutoregressive modelsGenerative adversarial networksGenerating molecules with MolGANSummaryFurther reading
Chapter 12: Learning from Heterogeneous Graphs
Technical requirementsThe message passing neural network frameworkIntroducing heterogeneous graphsTransforming homogeneous GNNs to heterogeneous GNNsImplementing a hierarchical self-attention networkSummaryFurther reading
Chapter 13: Temporal Graph Neural Networks
Technical requirementsIntroducing dynamic graphsForecasting web trafficIntroducing EvolveGCNImplementing EvolveGCNPredicting cases of COVID-19Introducing MPNN-LSTMImplementing MPNN-LSTMSummaryFurther reading
Chapter 14: Explaining Graph Neural Networks
Technical requirementsIntroducing explanation techniquesExplaining GNNs with GNNExplainerIntroducing GNNExplainerImplementing GNNExplainerExplaining GNNs with CaptumIntroducing Captum and integrated gradientsImplementing integrated gradientsSummaryFurther reading
Part 4: Applications
Chapter 15: Forecasting Traffic Using A3T-GCN
Technical requirementsExploring the PeMS-M datasetProcessing the datasetImplementing the A3T-GCN architectureSummaryFurther reading
Chapter 16: Detecting Anomalies Using Heterogeneous GNNs
Technical requirementsExploring the CIDDS-001 datasetPreprocessing the CIDDS-001 datasetImplementing a heterogeneous GNNSummaryFurther reading
Chapter 17: Building a Recommender System Using LightGCN
Technical requirementsExploring the Book-Crossing datasetPreprocessing the Book-Crossing datasetImplementing the LightGCN architectureSummaryFurther reading
Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications
Index
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Content preview from Hands-On Graph Neural Networks Using Python

7 Graph Attention Networks

Graph Attention Networks (GATs) are a theoretical improvement over GCNs. Instead of static normalization coefficients, they propose weighting factors calculated by a process called self-attention. The same process is at the core of one of the most successful deep learning architectures: the transformer, popularized by BERT and GPT-3. Introduced by Veličković et al. in 2017, GATs have become one of the most popular GNN architectures thanks to excellent out-of-the-box performance.

In this chapter, we will learn how the graph attention layer works in four steps. This is actually the perfect example for understanding how self-attention works in general. This theoretical background will allow us to implement a graph attention ...

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Hands-On Graph Neural Networks Using Python

by Maxime Labonne

7

Graph Attention Networks

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