Keywords:
Neural networks (Computer science).
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (148 pages)
Edition:
1st ed.
ISBN:
9783031015885
Series Statement:
Synthesis Lectures on Artificial Intelligence and Machine Learning Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=6356022
Language:
English
Note:
Cover -- Copyright Page -- Title Page -- Contents -- 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 -- Multi-Relational Data and Knowledge Graphs -- Reconstructing Multi-Relational Data -- Loss Functions -- Multi-Relational Decoders -- Representational Abilities -- 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 -- 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 -- Applications and Loss Functions -- GNNs for Node Classification -- GNNs for Graph Classification -- GNNs for Relation Prediction -- Pre-Training GNNs -- Efficiency Concerns and Node Sampling -- Graph-Level Implementations -- Subsampling and Mini-Batching -- Parameter Sharing and Regularization -- Theoretical Motivations -- GNNs and Graph Convolutions -- Convolutions and the Fourier Transform -- From Time Signals to Graph Signals -- Spectral Graph Convolutions -- Convolution-Inspired GNNs -- GNNs and Probabilistic Graphical Models -- Hilbert Space Embeddings of Distributions -- Graphs as Graphical Models -- Embedding Mean-Field Inference -- GNNs and PGMs More Generally -- GNNs and Graph Isomorphism -- Graph Isomorphism -- Graph Isomorphism and Representational Capacity -- The Weisfieler-Lehman Algorithm -- GNNs and the WL Algorithm -- Beyond the WL Algorithm -- Generative Graph Models -- Traditional Graph Generation Approaches -- Overview of Traditional Approaches -- Erdös-Rényi Model -- Stochastic Block Models -- Preferential Attachment -- Traditional Applications -- Deep Generative Models -- Variational Autoencoder Approaches -- Node-Level Latents -- Graph-Level Latents -- Adversarial Approaches -- Autoregressive Methods -- Modeling Edge Dependencies -- Recurrent Models for Graph Generation -- Evaluating Graph Generation -- Molecule Generation -- Conclusion -- Author's Biography -- Bibliography.
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