His research is on data management and machine learning for the emerging problems in large graphs. He is an IEEE senior member and an ACM distinguished speaker. Arijit is the recipient of the IBM Ph.D ...

First is Node2Vec, a popular graph embedding algorithm that uses neural networks to learn continuous feature representations for nodes, which can then be used for downstream machine learning tasks.

ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering. Written by George Anadiotis, Contributor Aug. 27, 2020 at 5:00 a.m. PT.

Neptune ML is the code name Amazon has given to the integration between its Neptune graph database and graph machine learning capabilities in SageMaker and DGL. AWS We'll have to wait to see if ...

Graph databases hold numerous attractions for financial services users, among them the ability to detect hidden patterns in data that could be harder to spot otherwise. Some financial institutions are ...

TigerGraph, maker of a graph analytics platform for data scientists, during its Graph & AI Summit event today introduced its TigerGraph ML (Machine Learning) Workbench, a new-gen toolkit that ...

Her research focuses on developing innovative algorithms and models that push the boundaries of machine learning, optimization, and artificial intelligence. Subscribe To Newsletters 7:08 ...

Graphs are among the most widely-used data structures in machine learning. Their power comes from the flexibility of capturing relations (edges) of collections of entities (nodes) which arise in a ...