PART I : FOUNDATION LEARNING CHAPTER 1 : INTRODUCTION TO GRAPH AND GRAPH REPRESENTATION LEARNING (GRL) 1. Introduction 1.1 What is a Graph? 1.2 Importance of Graph 1.3 Types of Graph 1.3.1 Directed Graph 1.3.

2 Undirected Graph 1.3.3 Weighted Graph 1.3.4 Multigraph 1.3.5 Complete Graph 1.3.

6 Planar and Bipartite Graph 1.3.7 Hypergraph 1.3.8 Hierarchical Graphs 1.3.9 Homogeneous and Heterogeneous Graphs 1.4 Overview of Graph Representation Learning 1.

4.1 Benefits of Graph Representation Learning (GRL) 1.5 Overview of Graph Connectivity 1.5.1 Its Definition 1.5.2. Importance of Graph Connectivity 1.

5.3. Role of Graph Connectivity 1.6 Foundation on Graph Neighborhood 1.6.1 Introduction 1.6.2 Properties of Graph Neighborhoods 1.

6.3 Types of Node/Graph Neighborhoods 1.6.3.1 Direct/Local Neighborhood (1-Hop Neighborhood) 1.6.3.2 K-Hop Neighborhood 1.

6.3.3 Ego Neighborhood 1.6.3.4 Attention-based neighborhoods 1.6.3.

5 Structural Neighborhood (Graphlets and Motifs) 1.6.3.6 Graphlets 1.7 Applications of Graph 1.7.1 Traffic Prediction in Transportation 1.7.

2 Pattern recognition 1.7.3 Social Network Analysis 1.7.4 Application of Graphs in Biology 1.7.5 Application of graphs in chemistry 1.8 Case studies of Graph Representation Learning (GRL) 1.

8.1 Case study in Social Network Analysis 1.8.2 Case Study in Fraud Detection for Financial Transactions 1.8.3 Case Study in Molecular Biology 1.9 Conclusion References CHAPTER 2 : FUNDAMENTAL CONCEPTS OF GRAPH STRUCTURES 2.1 Introduction to Graph Structures 2.

1.1 Characteristics of Graph Structure 2.2 Node-Level features 2.2.1 Node Degree 2.2.2 Node Centrality Measures 2.3 Structural-Level features 2.

3.1 Clustering Coefficient measure 2.3.2 Closed Triangles 2.3.3 Egographs 2.3.4 Motifs 2.

4 Graph-Level Features 2.4.1 Structural Properties of Graphs 2.4.1.1 Graph Degree and Density 2.4.1.

2 Graph Eccentricity and Modularity 2.5 Graph-Based Representation Techniques 2.5.1 Graph Clustering Coefficient 2.5.2 Bag of Nodes (BoN) 2.5.3 Weifeiler-Lehman Kernel 2.

5.4 Graphlets and Path-Based Methods 2.6 Graph Representation Matrix 2.6.1 Types of Graph Representation Matrices 2.6.1.1 Adjacency Matrix 2.

6.1.2 Incidence Matrix 2.6.1.3 Laplacian Matrix 2.6.1.

4 Transition Matrix 2.7 Conclusion References CHAPTER 3 OVERLAPPING NEIGHBORHOOD IN GRAPH 3.1 Definition and its importance 3.2 Methods for Detecting Neighborhood overlap 3.2.1 Local Overlap Measures 3.2.1.

1 Jaccard Similarity measure 3.2.1.2 Adamic-Adar Index 3.2.1.3 Resource Allocation Index 3.2.

1.4 Global Overlap Measures 3.2.1.5 Katz Index 3.2.1.6 SimRank 3.

2.1.7 PageRank-Based Similarity 3.3 Neighborhood Overlap Visualization Techniques 3.3.1 Venn Diagram 3.3.2 Node-Link Diagrams (Graph Representation) 3.

3.3 Heatmaps 3.3.4 Edge Bundling 3.3.5 Graph Embedding & Projection (t-SNE, UMAP) 3.3.6 Ego Networks (Subgraph Extraction ) 3.

4 Overlapping Neighborhoods in Heterogeneous Graphs 3.4.1 Introduction 3.4.2 Methods for Detecting Overlapping Neighborhoods in Heterogeneous Graphs A. Meta-Path Based Approaches B. Clustering-based methods 3.5 Case Studies on neighborhood overlap detection in real-world scenarios 3.

5.1 Application of Graph Neighborhood in Healthcare 3.5.2 Case studies on Social networks: overlapping user communities and friend recommendations 3.6 Conclusion References PART II : CORE GRAPH REPRESENTATION LEARNING CHAPTER 4 : GRAPH MACHINE LEARNING 4.1 Introduction to Graph Machine Learning (GML) 4.1.1 Key features of GML 4.

1.2 GML Vs traditional ML 4.2 Types of Tasks in GML 4.2.1 Supervised Graph Machine Learning (GML) tasks 4.2.1.1 Node Classification 4.

2.1.2 Link Prediction 4.2.1.3 Edge Classification 4.2.1.

4 Graph / Subgraph classification 4.2.2 Unsupervised Graph Machine Leaning tasks 4.2.2.1 Introduction 4.2.2.

2 Node Clustering A. Spectral Clustering B. Label Propagation Algorithm (LPA) C. Louvain Method for Community Detection 4.2.3 Case studies and Applications of GML Case study 1 : GML in Disease prediction Case study 2 : Drug Discovery and Development 4.3 Conclusion References CHAPTER 5 : GRAPH SAMPLING 5.1 Introduction 5.

1.1 Issues related to large and complex graphs 5.1.2 Definition with examples 5.1.3 Challenges of using sampling 5.2 Types of Graph Sampling 5.2.

1 Node Sampling 5.2.1.1 Overview of Node Sampling 5.2.1.2 Types of Node Sampling A. Uniform Node Sampling B.

Degree-based Node Sampling C. Stratified Node Sampling D. Snowball Node Sampling E. Random Walk-based Sampling 5.2.2 Edge Sampling 5.2.2.

1 Overview of Edge Sampling 5.2.2.2 Types of Edge Sampling A. Uniform Edge Sampling B. Weighted Edge Sampling C. Temporal Edge Sampling 5.2.

3 Neighborhood Sampling 5.2.3.1 Overview of Neighborhood Sampling 5.2.3.2 Types of Neighborhood Sampling A. K-Hop Neighborhood Sampling B.

Random Walk Neighborhood Sampling C. Layer-wise Neighborhood Sampling 5.2.4 Subgraph sampling 5.3 Case Studies 5.3.1 Case Study: Sampling Patient Networks for Disease Prediction 5.3.

2 Case Study: Sampling in Learning Management System (LMS) 5.3.3 Case Study: Community-Based Sampling for Efficient Social Network Analysis 5.4 Conclusion References CHAPTER 6 : GRAPH POOLING 6.1 Introduction to Graph Pooling 6.1.1 What is Graph Pooling? 6.1.

2 Why is Graph Pooling Important? 6.2 Types of Graph Pooling 6.2.1 Flat Pooling 6.2.1.1Methods of flat Pooling A. Sum Pooling B.

Mean Pooling C. Max Pooling D. Attention Pooling 6.2.1.2 Applications of Flat Pooling in Graph-Level Tasks 6.2.2.

Hierarchical Pooling 6.2.2.1 Types of Hierarchical Pooling 6.2.2.2Common techniques A. Top-K Pooling B.

DiffPool (Differentiable Pooling) C. SAGPool (Self-Attention Graph Pooling) D. MinCutPool (Minimum Cut Pooling) 6.2.3 Comparison between Flat pooling and Hierarchical pooling 6.3 Case Study on Graph Pooling 6.4 Conclusion References CHAPTER 7 : GRAPH NEURAL NETWORKS AND DEEP REPRESENTATION LEARNING 7.1 Foundation of Deep Representation Learning on graphs 7.

1.1 Basic concept 7.1.2 Challenges and importance of Deep Learning on Graphs 7.1.3 Approach of Deep Learning on Graph 7.1.4 Characteristics and Benefits of Deep Learning on Graph 7.

2 Introduction to Graph Neural Networks (GNNs) 7.2.1 GNN Architecture Overview 7.2.2 Message Passing in GNNs 7.2.3 Case Study on Graph Neural Network (GNNs) 7.2.

3.1 Case Study 1: Enhancing Credit Card Fraud Detection with CaT-GNN 7.2.3.2 Case Study: GNNs for Drug-Drug Interaction (DDI) Prediction in Healthcare 7.3 Fundamentals of Graph Convolution Networks (GCNs) 7.3.1 Core concepts and motivation for GCNs 7.

3.2 Workflow process of Graph Convolution Network (GCN) 7.4 Types of Graph Neural Networks 7.4.1 Graph Recurrent Neural Networks (GRNN) 7.4.1.1 Definition with illustration 7.

4.1.2 Working process of GRNN 7.4.1.3 GRNN Architecture 7.4.1.

4 Applications of GRNN 7.4.2 Graph Attention Networks (GAT) 7.4.2.1 Definition with illustration 7.4.2.

2 Working Process of Graph Attention Networks (GAT) 7.4.2.3 Architecture of GAT 7.4.2.4 Application of GAT 7.5 Applications of Deep Learning on Graph 7.

6 Conclusion References CHAPTER 8 : ADVANCED GRAPH NEURAL NETWORKS 8.1 Spatial-Temporal Graph Neural Networks (ST-GNNs) 8.1.1 Definition with illustration 8.1.2 Working Process of Spatial-Temporal Graph Neural Networks (ST-GNN) 8.1.3 Architecture of ST-GNNs 8.

1.4 Application of ST-GNNs 8.2 Dynamic Graph Neural Networks 8.2.1 Work Process of Dynamic GNNs 8.2.2 Example: Dynamic Graph - Social Interaction 8.2.

3 Dynamic Graph Neural Network models A. Temporal Graph Attention Network (TGAT) B. Dynamic Graph Convolutional Network (DyGCN) 8.3 Hypergraph Neural Networks 8.3.1 Architecture of HGNN 8.3.2 Working Process of HGNN 8.

4 Unsupervised Deep Learning Models 8.4.1 Graph Autoencoder (GAE) 8.4.1.1 GAE - Workflow with its Architecture 8.4.1.

2 GAE Algorithm 8.4.1.3 Applications of GAE 8.4.1.4 Advantages and Limitations of GAE 8.4.

2 Variational Auto Encoder (VAE) 8.4.2.1 VGAE - Workflow with its Architecture 8.4.2.2 Algorithm: Variational Graph Autoencoder (VGAE) 8.4.

2.3 Advantages and Limitations of VGAE 8.5 Conclusion References PART III : ADVANCED ANALYSIS AND TECHNIQUES CHAPTER 9 : GRAPH ALIGNMENT AND MATCHING 9.1 Introduction to Graph Alignment and Matching 9.2 Definition and purpose of graph align.