@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, ...
Is your feature request related to a problem? Please describe. The cluster graphical representations in resources page is currently a modal over the resources pages, w/o direct URL to get this modal ...
Abstract: In the field of graph self-supervised learning (GSSL), graph autoencoders and graph contrastive learning are two mainstream methods. Graph autoencoders aim to learn representations by ...
Abstract: Recently emerged label noise-resistant graph representation learning (LNR-GRL) has received increasing attention, which aims to enhance the generalization of graph neural networks (GNNs) in ...
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, ...
Data visualization is the graphical representation of information and data via visual elements like charts, graphs, and maps. It allows decision-makers to understand and communicate complex ideas to ...
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 ...
In today’s Data Storytelling Visualization journey, we learn to avoid making the same mistakes as the past; not every graph/chart needs to highlight groundbreaking insights, and we must deal with the ...
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