This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

Abstract: Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional ...

Abstract: Graph spectral filtering relies on a representation matrix to define the frequency-domain transformations. Conventional approaches use fixed graph representations, which limit their ...

1 Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, United States 2 Department of Computer Science, Washington University in St. Louis, St. Louis, MO, United ...

Graphs and data visualizations are all around us—charting our steps, our election results, our favorite sports teams’ stats, and trends across our world. But too often, people glance at a graph ...

In the age when data is everything to a business, managers and analysts alike are looking to emerging forms of databases to paint a clear picture of how data is delivering to their businesses. The ...

If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...

Graph generation is a complex problem that involves constructing structured, non-Euclidean representations while maintaining meaningful relationships between entities. Most current methods fail to ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Different molecular representations exist to provide visual and mathematical means to ...

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...