Welcome to the official website of the IEEE Task Force on Learning for Graphs (LG) promoted by the IEEE Technical Committee on Neural Networks. The task force, originally named as IEEE Task Force on Structured Data (LEARN4SD),  was formed in 2020. It is updated to the new name Learning for Graphs in 2023.

Graph data are a natural representation for compound information made of atomic information pieces, i.e. the nodes (and their labels), and their relationships represented by the edges (and their labels).  

Graphs are the most general and complex form of structured data, which allow to represent networks of interacting elements, e.g. social or metabolomics graphs, as well as data where topological variations define important and interesting properties, e.g. molecular compounds.  

Being able to process data by accounting for these rich structured forms provides a fundamental advantage when it comes to identifying data patterns useful for predictive and/or explorative analyses. This has motivated a recent increasing interest of the machine learning community into the development of learning models for structured information.  

Task Force Goals

The broad aim of the task force is to promote events, research and dissemination activities for the community working on machine learning for structured data.  Please regularly check our Activities page.

The detailed goals of the task force are


The task force focuses on the vertical theme of learning models and downstream tasks on graph data, considering many different paradigms of learning-based approaches including, but not limited, to deep learning and neural networks.

A (non-exhaustive) list of topics of interest follows:

Please feel free to contact us to discuss and propose further topics!