TigerGraph expands its graph data library with 20 new algorithms

TigerGraph, a company that provides a graph database and analytics software, has expanded its data science library with 20 new algorithms, bringing its total to more than 50 algorithms. Graph databases like TigerGraph have become increasingly popular. They are particularly effective at letting data scientists analyze relationships among millions or billions of entities, and they outperform other types of databases for many deep learning applications. Of course this promising market sees plenty of competitors -- startups like Neo4j, MongoDB, and DataStax, as well as giants like Oracle and Amazon. What sets TigerGraph's product apart is that it is open source, in-database, scalable, and uniquely centered around graph data science. This company is first to offer a distributed native graph database as well, and has gained traction in enterprises.