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 ...

“Data and analytics leaders should use graph analytics ... 65 ready-to-use graph algorithms and is optimized for high-performance AI applications, with support for parallel workflows ensuring ...

and visualize data. The solution includes a selection of more than 65 ready-to-use graph algorithms and is optimized for high-performance applications and parallel workflows. Users pay only for the ...

title = {Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable ... since the compressed graph data can be mmap'd. Application performance will be affected if the file is not ...

Moreover, a few parallel dynamic graph algorithms (such as the graph connectivity ... Moreover, the total memory required to store these data structures of this sequential dynamic APSP algorithm ...

From Table 2, it can be seen that when storing the same distribution network structure data, the serial computation ... networks based on the Neo4j graph model and a hierarchical forward-backward ...

RelationalAI’s technology comes with algorithms to help drive AI-based decision making from the data, graph analytics libraries, SDKs for programming languages like SQL, Python, and Java, various ...

or using unsupervised methods like graph algorithms to sift through data and figure out patterns that you should be looking at. It can also mean using supervised machine learning where you’re ...