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 -- Backgroun...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[San Rafael]
Morgan & Claypool
[2020]
|
| Series: | Synthesis lectures on artificial intelligence and machine learning
46 |
| 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 |
| Physical Description: | XVII, 141 Seiten |
| ISBN: | 9781681739656 9781681739632 |
Staff View
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:42:31Z |
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| institution | BVB |
| isbn | 9781681739656 9781681739632 |
| language | English |
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| physical | XVII, 141 Seiten |
| publishDate | 2020 |
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| publisher | Morgan & Claypool |
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| series | Synthesis lectures on artificial intelligence and machine learning |
| series2 | Synthesis lectures on artificial intelligence and machine learning |
| spelling | Hamilton, William L. Verfasser (DE-588)1226714064 aut Graph representation learning William L. Hamilton, McGill University and Mila-Quebec Artificial Intelligence Institute [San Rafael] Morgan & Claypool [2020] © 2020 XVII, 141 Seiten txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on artificial intelligence and machine learning 46 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 9781681739656 Erscheint auch als Online-Ausgabe 9781681739649 Synthesis lectures on artificial intelligence and machine learning 46 (DE-604)BV035750800 46 |
| spellingShingle | Hamilton, William L. Graph representation learning Synthesis lectures on artificial intelligence and machine learning |
| title | Graph representation learning |
| title_auth | Graph representation learning |
| title_exact_search | Graph representation learning |
| title_exact_search_txtP | Graph representation learning |
| title_full | Graph representation learning William L. Hamilton, McGill University and Mila-Quebec Artificial Intelligence Institute |
| title_fullStr | Graph representation learning William L. Hamilton, McGill University and Mila-Quebec Artificial Intelligence Institute |
| title_full_unstemmed | Graph representation learning William L. Hamilton, McGill University and Mila-Quebec Artificial Intelligence Institute |
| title_short | Graph representation learning |
| title_sort | graph representation learning |
| volume_link | (DE-604)BV035750800 |
| work_keys_str_mv | AT hamiltonwilliaml graphrepresentationlearning |