BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

But researchers commonly analyze graphs with billions or more connections. And so they’ve developed algorithms that assign colors for them. These algorithms, however, are slow and had been “stuck more ...

The accompanying paper gives an algorithm for negative-length shortest paths (with poly-bounded edge weights) that runs in nearly linear time. This algorithm delves into some of the most important ...

Graph algorithms are integral for solving complex programming challenges. They provide methods for handling data relationships through graphs, which are essential in fields like network analysis, ...

Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that ...

The purpose of this is to minimize the negative log-likelihood loss while constraining the weights of the model through the regularization term in order to ... Wang X, Ye Z, Li M and Zhao H (2023) ...

I have been optimizing sites for search engines since 1998, the year Google was incorporated. In those 25 years, I never saw an update as big as the 2023 E-E-A-T Knowledge Graph update.

Graph theory is the study of networks and connections between variables. In the real world it has uses from powering search engine algorithms to fixing airline scheduling. Now, scientists are ...

ML algorithms can recognize patterns and anomalies within graph structures, while AI techniques process unstructured data like fraud reports or images of fraudulent documents, enriching the ...