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.

Graph data science is an emerging field with a lot of promise, but it’s being hamstrung by the need for practitioners to have lots of data engineering and ETL skills. Now Neo4j is hoping to drive that ...

Neo4j Graph Data Science makes it easy for data scientists to work within their existing data pipeline of tools across their ecosystem. Data scientists can use Neo4j Graph Data Science on-premises, ...

The COVID GRAPH and Open Research Knowledge Graph (ORKG) teams have focused on COVID-19, and emphasized annotation and structure, respectively. Connected Papers seems to expand coverage, and ...

ML Workbench essentially provides the Jupyter data science notebook experience directly within the TigerGraph database. Plus, it’s loaded with 55 pre-built graph algorithms that have been adapted by ...

The convergence of graph theory, social network analysis and multimedia data mining has emerged as a vibrant interdisciplinary field. Graph models provide a structured representation of ...

This is a guest blogpost by Emil Eifrem, CEO, Neo4j. In his view, four major trends driving interest in graph technology are starting to surface. Let's look ahead to what could be in store for the ...

The goal is to design a graph neural network based recommendation system for scroll paintings (folk art) that can run on decentralized compute environments (such as peer-to-peer systems). Requires ...

The Michigan Tech Advantage The Michigan Tech Data Science MS provides a broad-based education in data mining, predictive analytics, cloud computing, data-science fundamentals, communication, and ...

Gonzalo Arce College of Engineering — Electrical and Computer Engineering [email protected] Machine learning, data science, graph signal and data processing, geometrical neural networks, statistical ...