2021 Predictions: Data Science
By Datanami - December 23, 2020
Explore: artificial intelligence • data science • data scientists • Data Visualization • machine learning
21 Reading Recommendations for 2021
By Enzo
January 4, 2021 6 mins read
Check out the 21 books to add to your 2021 reading list in 2021, on topics including graph technology and AI/machine learning.
Explore: artificial intelligence • Data Cloud • fintech • for dummies • fullstack • GRANDstack • graph data science • Graph Theory • machine learning • reading
The Top 7 Neo4j Webinars of 2020
By Jessica Baumgardner
December 28, 2020 3 mins read
Check out the top Neo4j webinars from 2020, exploring key topics like human trafficking, responsible AI, graph data science and more.
Explore: 4.0 • ai • artificial intelligence • Connected Data • connections • GDS library • graph data science • graph data science library • Graphs4Good • human trafficking
Skeptical about AI-derived recommendations? Here are some tips to get you started
By IT World Canada - December 14, 2020
Explore: AI / Machine Learning • artificial intelligence • data scientists • Strategy
The Importance of Graph Databases in Business Analytics
By Dataversity - November 18, 2020
Explore: artificial intelligence • Augmented Analytics • business analytics • graph database • machine learning
Podcast: How to speed up AI solutions to target and identify money launderers
By IBS Intelligence - November 18, 2020
Explore: ai • anti-money laundering • artificial intelligence • fraud detection • podcast
AI use cases in healthcare for Covid-19 and beyond
By Information Age - September 4, 2020
Explore: ai • artificial intelligence • graph database • graph technology • machine learning
Combating Money Laundering: Graph Tech Fights Serious Crimes
By David Penick
August 10, 2020 < 1 min read
Read the first blog in Neo4j’s new six-part series on how Neo4j’s graph technology can help you combat money laundering.
Explore: AML • artificial intelligence • machine learning • money laundering • natural language processing • Panama Papers
Content is king but context is queen
By MicroScope - July 28, 2020
Explore: ai • AI / Machine Learning • AI/ML • artificial intelligence • context • contextual data
How to implement AI and advanced analytics, and observe the technologies’ transformational impact
By Information Age - July 13, 2020
Explore: ai • AI / Machine Learning • AI/ML • Amy Hodler • Analytics • artificial intelligence • ML
The Power of Graph Data Science: 5-Minute Interview with Alicia Frame
By David Penick
July 10, 2020 3 mins read
Check out this week’s 5-Minute Interview with Alicia Frame, Senior Data Scientist at Neo4j, about the Neo4j Graph Data Science Library.
Explore: 5-minute interview • ai • artificial intelligence • data science • graph algorithms • graph data science • python
Building the Enterprise Knowledge Graph
By Jocelyn Hoppa
July 8, 2020 11 mins read
Learn how to build and implement an enterprise knowledge graph from the groun up using the Google Drive API and Neo4j Python.
Explore: artificial intelligence • cypher • enterprise data • Knowledge Graph • knowledge management technology • machine learning • python
Your World Seen Through Your Eyes with Neo4j Bloom
By Corin Gainey
June 17, 2020 25 mins read
Andreas Kolleger discusses Neo4j Bloom and the possibilities it opens up for enterprise organizations across their departments.
Explore: artificial intelligence • Big Data • Connected Data • data model • Data Visualization • graphconnect • Knowledge Graph • machine learning • neo4j • rdbms
Graph-Powered Recommendations: How Smart Recommendations Drive Value
By David Penick
May 11, 2020 2 mins read
Read blog one in Neo4j’s five-part series on how graph-powered recommendation engines drive value for enterprise businesses.
Explore: artificial intelligence • ecommerce • graph-based technologies • machine learning • personalized experience • Recommendation Engines • Recommendations • relational
Financial Fraud Detection with Graph Data Science: Identifying Fraud Rings
By David Penick
May 4, 2020 5 mins read
Read blog four in this series on how financial services enterprises are using Neo4j’s graph technology to prevent and detect financial fraud.
Explore: artificial intelligence • Cypher Query • data science • Financial Fraud Detection • Fraud Rings • graph algorithms • Graph Analytics • graph data science • Neo4j Bloom • python
How Previa Is Reimagining Healthcare
By Corin Gainey
April 29, 2020 21 mins read
Editor’s Note: This presentation was given by Alessandro Svensson and Sarah Johnnesson at GraphConnect New York in September 2018. Presentation Summary Alessandro Svensson is the Head of Neo4j Innovation Labs within the U.S. He and Sarah Johnnesson of Previa discuss… Read more →
Explore: ai • artificial intelligence • Big Data • Connected Data • cypher • data model • Data Visualization • graphconnect • machine learning • neo4j
Financial Fraud Detection with Graph Data Science: Analytics and Feature Engineering
By David Penick
April 20, 2020 2 mins read
Read blog two on how financial services enterprises are using Neo4j’s graph technology to prevent and detect financial fraud.
Explore: artificial intelligence • betweeness centrality • community detection algorithm • cypher • cypher queries • data science • Financial Fraud Detection • fraud detection • graph algorithms • Graph Analytics
Announcing Neo4j for Graph Data Science
By Jocelyn Hoppa
April 8, 2020 7 mins read
Discover the benefits of the Neo4j for Graph Data Science, the first data science environment built to harness the predictive power of relationships.
Explore: ai • Analytics • artificial intelligence • Bloom • data scientists • graph algorithms • Graph Analytics • graph data science • graph database • graph visualization
Fujitsu Laboratories of America Partners with Larus to Provide Enhanced Graph AI Technologies to Neo4j Customers
By Jocelyn Hoppa
April 7, 2020 3 mins read
Fujitsu Laboratories is building on Larus’s expertise in Neo4j customer requirements and application domain knowledge with advanced graph AI technologies.
Explore: ai • artificial intelligence • Connected Data • Deep Tensor • Fujitsu • Graph Databases • graph technology • knowledge graphs • Larus • ML
Machine Learning Algorithms
By Corin Gainey
March 18, 2020 11 mins read
Lauren Shin, Neo4j Developer Relations Intern, introduces machine learning and offers three approaches to better analyze ML data.
Explore: artificial intelligence • Connected Data • data model • Data Visualization • Graph Analytics • graph database • graph visualization • graphconnect • machine learning • neo4j
#GraphCast: Responsible AI
By Jocelyn Hoppa
March 13, 2020 < 1 min read
This week’s #GraphCast features Responsible AI where Amy Hodler discusses ethical development and application of artificial intelligence and technology.
Explore: artificial intelligence • ethical AI • graph database • GraphCast • Responsible AI • responsible technology
GraphConnect 2020 Agenda: Everything You Need to Know
By Rachel Howard
March 6, 2020 < 1 min read
The health and safety of our community, partners and employees are of the utmost importance to Neo4j. After monitoring the COVID-19 situation closely, we have decided to postpone GraphConnect 2020 in New York City to 2021. We are actively scoping… Read more →
Explore: artificial intelligence • biotech • data discovery • Data Visualization • digital transformation • graph technology • graphconnect • Healthcare • Knowledge Graph • machine learning
Ontologies in Neo4j: Semantics and Knowledge Graphs
By Corin Gainey
February 19, 2020 12 mins read
Jesús Barrasa presents the two biggest uses of ontologies in graphs using Neo4j and NeoSemantics; inferences and interoperability.
Explore: artificial intelligence • Big Data • Connected Data • graph technology • Knowledge Graph • machine learning • neo4j • ontologies • ontology
Google Releases “Largest Ever” Map of Brain Connectivity
By Computer Business Review - January 24, 2020
Explore: ai • AI / Machine Learning • artificial intelligence • Brain • data connectivity • google • Graph Databases
Connecting Healthcare
By Allison Wu
January 28, 2020 20 mins read
Learn about connecting healthcare data, including its challenges and how graph databases might be able to help solve healthcare’s wicked problems.
Explore: artificial intelligence • Connected Data • data silos • data variability • Graph Databases • graphconnect • Healthcare • integration • machine learning • Relational Databases