PyTorch

Multi-relational graphs are complex structures that represent relationships between different kinds of data. Knowledge Graph Embeddings (KGE) are used to project this multi-relational graph data into a low-dimensional space where we can......

Temporal Graph Neural Networks (TGNNs) have recently emerged as a powerful method for working with dynamic graph data. Unlike static graph neural networks, TGNNs can handle changes over time in graph structures, which is crucial for......

Graph Isomorphism Networks (GINs) have emerged as a powerful tool for graph-based machine learning tasks because of their capability to effectively differentiate between different graph structures. In this article, we explore how to......

Community detection is a complex problem in graph theory which involves dividing a network into clusters of nodes. These clusters, or 'communities', contain nodes that are more densely connected internally than with the rest of the......

Graph neural networks (GNNs) have gained significant popularity for their ability to model complex relationships in graph-structured data. In many real-world applications, such graphs are often heterogeneous, containing multiple types of......

In the era of deep learning, the need to handle graph-structured data efficiently is paramount. PyTorch Geometric, a library built upon PyTorch, is a go-to solution for this. It provides tools to work with graph data easily, leveraging......

Graph Neural Networks (GNNs) have gained significant traction in recent years due to their ability to learn from graph-structured data. A novel approach that has emerged is the application of self-supervised learning techniques to GNNs,......

Graph Neural Networks (GNNs) have become a powerful tool for processing graph-structured data, thanks to their ability to learn representations of nodes and relationships. However, as GNN models grow in complexity and dataset sizes......

Recommender systems have become an integral part of our online experiences, assisting us in finding relevant items among vast amounts of data. To improve these systems, integrating graph-based models has shown substantial promise. Graphs......

Graph Neural Networks (GNNs) have emerged as an effective technique for molecular property prediction. By utilizing graph structures that represent the atoms as nodes and bonds as edges, GNNs can capture the complex relationships inherent......

Link prediction is a critical task in the analysis and understanding of social networks. Given a set of nodes and a partially observed set of edges between them, link prediction aims to infer the existence of missing links. It finds......

Graph embedding is a compelling area of study that deals with representing nodes, edges, and sometimes entire subgraphs in a continuous vector format. One of the leading technologies in this domain is GraphSAGE (Graph Sample and......