Graph Neural Networks

This series introduces foundational graph theory concepts—such as nodes, edges, adjacency matrices and highlights the unique characteristics of graph data.
We will explore key applications of GNNs including their roles in social networks, molecular research, transportation, and knowledge graphs.

In this series, we will explore the fundamentals of graph theory and network science, and learn how to analyze and model complex networks, focusing on techniques like community detection and graph-based algorithms.

In this series, we will explore the Laplacian Matrix as a powerful tool for network representation.

The Laplacian matrix, created by subtracting the adjacency matrix from the degree matrix, is essential in understanding the structure and properties of networks.

We will learn how it captures important information about connectivity, node relationships, and graph smoothness.

This series explores node embeddings in graph-based learning, focusing on methods like Spectral Embedding and Random Walk-Based Approaches (e.g., DeepWalk, node2vec) for transforming graph nodes into efficient vector representations.

We will learn how node embeddings are applied in real-world tasks such as node classification, clustering, and link prediction.

This series introduces the Message Passing Framework in Graph Neural Networks (GNNs), where node representations are updated by combining node features with information from neighboring nodes.

We will learn how GNNs balance graph structure and node features to create richer embeddings for tasks like classification, clustering, and prediction.

We will also explore practical applications in areas such as citation networks, social graphs, and traffic analysis.

This series explores Graph Neural Networks (GNNs), focusing on key topics like the Weisfeiler-Lehman (WL) Test for graph isomorphism and aggregation methods (e.g., summation, max-pooling) to combine node and neighbor information.

We will cover Graph Isomorphic Networks (GINs), which outperform models like GCNs and GATs in expressiveness and accuracy, using summation-based aggregation with scaling and nonlinearity.

We will also explore GNN applications in recommendation systems and protein networks, with an emphasis on inductive learning and future advancements in generative models.

This series delves into Graph Attention Networks (GATs) and Graph Convolutional Networks (GCNs), two powerful models for graph-based learning.

GATs use attention mechanisms to weigh node connections, allowing the model to focus on more important neighbors for better representation learning. GCNs, on the other hand, aggregate node features using convolutional layers, capturing local graph structure.

We will explore the strengths and differences of these models in tasks like node classification, link prediction, and graph-based clustering, along with practical applications and implementation techniques.

This series explores Graph Representation in Recommendations using Graph Convolutional Networks (GCNs).

We will also cover GCN architecture for enhancing node embeddings, combining GCNs with GRUs for sequential tasks, and using PyTorch Geometric for model implementation.