book

Hands-On Graph Neural Networks Using Python

by Maxime Labonne

April 2023

Intermediate to advanced

354 pages

8h 22m

English

Packt Publishing

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Why graphs?Why graph learning?Why graph neural networks?SummaryFurther reading
Technical requirementsIntroducing graph propertiesDirected graphsWeighted graphsConnected graphsTypes of graphsDiscovering graph conceptsFundamental objectsGraph measuresAdjacency matrix representationExploring graph algorithmsBreadth-first searchDepth-first searchSummary
Technical requirementsIntroducing Word2VecCBOW versus skip-gramCreating skip-gramsThe skip-gram modelDeepWalk and random walksImplementing DeepWalkSummaryFurther reading
Technical requirementsIntroducing Node2VecDefining a neighborhoodIntroducing biases in random walksImplementing Node2VecBuilding a movie RecSysSummaryFurther reading
Technical requirementsIntroducing graph datasetsThe Cora datasetThe Facebook Page-Page datasetClassifying nodes with vanilla neural networksClassifying nodes with vanilla graph neural networksSummaryFurther reading
Technical requirementsDesigning the graph convolutional layerComparing graph convolutional and graph linear layersPredicting web traffic with node regressionSummaryFurther reading

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
Technical requirementsIntroducing GraphSAGENeighbor samplingAggregationClassifying nodes on PubMedInductive learning on protein-protein interactionsSummaryFurther reading
Technical requirementsDefining expressivenessIntroducing the GINClassifying graphs using GINGraph classificationImplementing the GINSummaryFurther reading
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
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
Technical requirementsThe message passing neural network frameworkIntroducing heterogeneous graphsTransforming homogeneous GNNs to heterogeneous GNNsImplementing a hierarchical self-attention networkSummaryFurther reading
Technical requirementsIntroducing dynamic graphsForecasting web trafficIntroducing EvolveGCNImplementing EvolveGCNPredicting cases of COVID-19Introducing MPNN-LSTMImplementing MPNN-LSTMSummaryFurther reading
Technical requirementsIntroducing explanation techniquesExplaining GNNs with GNNExplainerIntroducing GNNExplainerImplementing GNNExplainerExplaining GNNs with CaptumIntroducing Captum and integrated gradientsImplementing integrated gradientsSummaryFurther reading
Technical requirementsExploring the PeMS-M datasetProcessing the datasetImplementing the A3T-GCN architectureSummaryFurther reading
Technical requirementsExploring the CIDDS-001 datasetPreprocessing the CIDDS-001 datasetImplementing a heterogeneous GNNSummaryFurther reading
Technical requirementsExploring the Book-Crossing datasetPreprocessing the Book-Crossing datasetImplementing the LightGCN architectureSummaryFurther reading
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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

activation function 107

Adamic-Adar index 165

adjacency list 22

adjacency matrix 20-22

adjacent 18

aggregate function 148

aggregation 129

aggregator 129

LSTM aggregator 129

mean aggregator 129

pooling aggregator 129

anomaly detection 275