Intro -- Preface -- Acknowledgments -- Introduction -- What is a Graph? -- Multi-Relational Graphs -- Feature Information -- Machine Learning on Graphs -- Node Classification -- Relation Prediction -- Clustering and Community Detection -- Graph Classification, Regression, and Clustering -- Backgroun...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Hamilton, William L. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	[San Rafael] Morgan & Claypool [2020]
Schriftenreihe:	Synthesis lectures on artificial intelligence and machine learning 46
Zusammenfassung:	Intro -- Preface -- Acknowledgments -- Introduction -- What is a Graph? -- Multi-Relational Graphs -- Feature Information -- Machine Learning on Graphs -- Node Classification -- Relation Prediction -- Clustering and Community Detection -- Graph Classification, Regression, and Clustering -- Background and Traditional Approaches -- Graph Statistics and Kernel Methods -- Node-Level Statistics and Features -- Graph-Level Features and Graph Kernels -- Neighborhood Overlap Detection -- Local Overlap Measures -- Global Overlap Measures -- Graph Laplacians and Spectral Methods -- Graph Laplacians -- Graph Cuts and Clustering -- Generalized Spectral Clustering -- Toward Learned Representations -- Node Embeddings -- Neighborhood Reconstruction Methods -- An Encoder-Decoder Perspective -- The Encoder -- The Decoder -- Optimizing an Encoder-Decoder Model -- Overview of the Encoder-Decoder Approach -- Factorization-Based Approaches -- Random Walk Embeddings -- Random Walk Methods and Matrix Factorization -- Limitations of Shallow Embeddings -- Multi-Relational Data and Knowledge Graphs -- Reconstructing Multi-Relational Data -- Loss Functions -- Multi-Relational Decoders -- Representational Abilities -- Graph Neural Networks -- The Graph Neural Network Model -- Neural Message Passing -- Overview of the Message Passing Framework -- Motivations and Intuitions -- The Basic GNN -- Message Passing with Self-Loops -- Generalized Neighborhood Aggregation -- Neighborhood Normalization -- Set Aggregators -- Neighborhood Attention -- Generalized Update Methods -- Concatenation and Skip-Connections -- Gated Updates -- Jumping Knowledge Connections -- Edge Features and Multi-Relational GNNs -- Relational Graph Neural Networks -- Attention and Feature Concatenation -- Graph Pooling -- Generalized Message Passing -- Graph Neural Networks in Practice
Beschreibung:	XVII, 141 Seiten
ISBN:	9781681739656 9781681739632