Prasun Chaudhuri speaks to expert Subrata Das about the opportunities. Das is currently an adjunct faculty member at ...

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

Neo4j Aura Graph Analytics comes with more than 65 ready-to-use graph algorithms and is optimized for high-performance AI applications, with support for parallel workflows ensuring any app can ...

Users can deploy and scale graph analytics workloads end-to-end, enabling them to collect, organize, analyze, and visualize data. The solution includes a selection of more than 65 ready-to-use graph ...

This repository contains code for our SPAA paper "Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable" (SPAA'18). It includes implementations of the following parallel graph ...

He has made deep and wide-ranging contributions to many areas of parallel computing including programming languages, compilers, and runtime systems for multicore, manycore and distributed computers.

In recent years, the Massively Parallel Computation (MPC) model has gained significant attention. However, most of distributed and parallel graph algorithms in the MPC model are designed for ...

Fast power flow calculation for distribution networks based on graph models and hierarchical forward-backward sweep parallel algorithm. Xinrui Wang* Wengang Chen ... Wang, H., and Zhao, D. (2017).

“You have predictive analytics, you have prescriptive analytics, you have graph analytics, you have planning, you have rule-based reasoning,” Aref tells The Next Platform.. “The idea here is – ...

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.