Graph representation learning
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 …
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- Full title :Graph representation learning / William L. Hamilton, McGill University and Mila-Quebec Artificial Intelligence Institute.
Summary
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.
Read More ↓Edition Statements
- Publication Date:© 2020
Publication Statements
- Publisher(s):Morgan et Claypool
- Place of Publication:San Rafael
- Language:eng
- Publication Date:© 2020
The Physical Object
- Description:XVII, 141 Seiten
Edition Identifiers
- ISBN:9781681739656, 1681739658, 9781681739632, 1681739631
- oclc:on1245331570
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