Abstract: Graph representation learning is an emerging area for graph analysis and inference. However, existing approaches for large-scale graphs either sample nodes in sequential walks or manipulate ...

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...

An interactive toolbox for standardizing, validating, simulating, reducing, and exploring detailed biophysical models that can be used to reveal how morpho-electric properties map to dendritic and ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

About four-in-ten U.S. adults (42%) say the Republican Party represents the interests of people like them very or somewhat well. A similar share (40%) say this about the Democratic Party. But ...

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 ...

The graph database market, driven by AI, is growing at a rate of almost 25% annually. Graph databases support knowledge graphs, providing visual guidance for AI development. There are multiple ...

CEO Sam Altman called a strange graph in its GPT-5 presentation a ‘mega chart screwup.’ CEO Sam Altman called a strange graph in its GPT-5 presentation a ‘mega chart screwup.’ is a senior reporter ...

Last month, I decided to see Thunderbolts*, the newest installment in the expansive Marvel Cinematic Universe (MCU). I went to the theater without my 11-year-old, not sure whether or not this movie ...

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...