GitHub

This repository is an official PyTorch (Geometric) implementation of FDGNN in "Disentangled contrastive learning for fair graph representations".

@article{zhang2025disentangled, title={Disentangled contrastive learning for fair graph representations}, author={Zhang, Guixian and Yuan, Guan and Cheng, Debo and Liu, Lin and Li, Jiuyong and Zhang, ...
ascopubs.org

Knowledge Representation of a Multicenter Adolescent and Young Adult Cancer Infrastructure: Development of the STRONG AYA Knowledge Graph

We structured the STRONG AYA case-mix and core outcome measures concepts and their properties as knowledge graphs. Having identified the corresponding standard terminologies, we developed a semantic ...
GitHub

CS 886: Graph Neural Networks

This is a 3-page paper, along with (if relevant) the source code of your project, including instructions on how to run it. You may use your midterm project as a foundation for your final project, ...
C&EN

Graph Representations of Nanostructured Hedgehog Particles with Variable Complexity

Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States Amphionic Inc, Ann Arbor, Michigan 48109, United States Department of Chemical Engineering, ...
Scientific Research Publishing

Cini, A., Marisca, I. and Alippi, C. (2022) Filling the Gaps: Multi-Variate Time Series Imputation by Graph Neural Networks. International Conference on Learning ...

ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
IEEE

Learning Convolutional Representation of Cross-Spatial Multiscale Graphs for Human Skeleton Action Recognition

Abstract: Human skeleton can be classified as a form of graph-structured data. GCNs have gained popularity in human skeleton action recognition due to their exceptional capacity to handle graph data.
marktechpost

GraphIC: A Novel Machine Learning Approach that Leverages Graph-based Representations of Reasoning Processes Coupled with Bayesian Networks (BNs) to Select In-Context Examples ...

In-context learning (ICL) enables LLMs to adapt to new tasks by including a few examples directly in the input without updating their parameters. However, selecting appropriate in-context examples ...