Описание: Introduction
1.1 introduction
1.2 Notations used in Book
1.3 Contents covered in this book
2 Representations of Networks
2.1 Introduction
2.2 Networks Represented as Graphs
2.3 Data Structures to Represent Graphs
2.3.1 Matrix Representation
2.3.2 Adjacency List 2.4 Network Embeddings
2.5 Evaluation Datasets
2.5.1 Evaluation Datasets
2.5.2 Evaluation Metrics
2.6 Machine Learning Downstream Tasks
2.6.1 Classification
2.6.2 Clustering 2.6.3 Link Prediction (LP)
2.6.4 Visualization
2.6.5 Network Reconstruction
2.7 Embeddings based on Matrix Factorization
2.7.1 Singular Value Decomposition (SVD)
2.7.2 Matrix Factorization based Clustering
2.7.3 Soft Clustering as Matrix Factorization 2.7.4 Non-negative Matrix factorization (NMF)
2.8 Word2vec
2.8.1 Skipgram model
2.9 Learning Network Embeddings
2.9.1 Supervised Learning
2.9.2 Unsupervised Learning
2.9.3 Node and Edge Embeddings 2.9.4 Graph Embedding
2.10 Summary
3 Deep Learning
3.1 Introduction
3.2 Neural Networks
3.2.1 Perceptron
3.2.2 Characteristics of Neural Networks
3.2.3 Multilayer Perceptron Networks
3.2.4 Training MLP Networks
3.3 Convolution Neural Networks 3.3.1 Activation Function
3.3.2 Initialization of Weights
3.3.3 Deep Feedforward Neural Network
3.4 Recurrent Networks
3.4.1 Recurrent Neural Networks
3.4.2 Long Short Term Memory
3.4.3 Different Gates used by LSTM 3.4.4 Training of LSTM Models
3.5 Learning Representations using Autoencoders
3.5.1 Types of Autoencoders
3.6 Summary
References
4 Embedding Nodes and Edge
4.1 Introduction
4.2 Representation of Node and Edges as Vectors
4.3 Embeddings based on Random Walks
4.4 Embeddings based on Matrix Factorization 4.5 Graph Neural Network Models
4.6 State of the art algorithms
4.7 Evaluation methods and Machine Learning tasks
4.8 Summary
References
5 Embedding Graphs
5.1 Introduction
5.2 Representation of Graphs as Vectors
5.3 Graph Representation using Node Embeddings
5.4 Graph Pooling Techniques 5.4.1 Global Pooling Methods
5.4.2 Hierarchical Pooling Methods
5.5 State of the art algorithms
5.6 Evaluation methods and Machine Learning tasks
5.7 Summary
References
