Open Source AI Project

Towards-graph-foundation-models

A compilation of literature resources related to graph foundation models.

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This GitHub project serves as a comprehensive repository dedicated to literature on graph foundation models, targeting researchers, academics, and practitioners in the field of machine learning, especially those with a focus on graph-based approaches. The primary goal of the project is to curate a vast array of resources, including research papers, articles, tutorials, and datasets, that explore the development, implementation, and application of foundational models utilizing graph structures.

Graph foundation models represent a specialized subset of machine learning that emphasizes the use of graph structures for data representation and analysis. Graphs, consisting of nodes (vertices) and edges (links), are powerful tools for modeling complex relationships and interactions in various types of data, from social networks and biological networks to knowledge graphs and beyond. These models leverage the inherent connectivity and topology of graphs to perform tasks such as node classification, link prediction, graph classification, and graph generation.

By compiling resources related to these models, the project aims to provide a centralized platform for sharing cutting-edge knowledge and advancements in the field. This facilitates the exchange of ideas, promotes collaboration among researchers, and supports the advancement of graph-based machine learning techniques. The repository is intended to be an evolving resource, regularly updated with new findings, methodologies, and applications of graph foundation models, thus ensuring its relevance and utility for ongoing research and innovation in the domain.

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