In this week’s newsletter, researchers at UCLA DataResolutions set out to train a model to predict team assignments of their members based on who they know in the organization. The project integrates a graph deep learning pipeline with a knowledge graph to create a complete stack of network analysis tools, and then uses these tools for analysis of social networks. By integrating graphs with the DGL framework, a Python package dedicated to deep learning on graphs, the authors combine the visualization prowess and classical graph algorithms of Neo4j with the end-to-end deep learning models of DGL. The repository for this project is hosted on Github.
PS: Don’t forget to code and golf before September 15! Neo4j Code Golf challenge is your chance to win prizes totaling $27,000!!
FEATURED COMMUNITY MEMBER: Niklas Saers
Niklas is a skilled lead solutions architect, cloud and mobile, who can handle a variety of tech stacks. He was first introduced to Neo4j in 2011 and got involved with the port of Theo to Swift using the Bolt protocol. He built Bolt-swift, as well as Packstream-Swift. He is an avid contributor on Github and the co-maintainer of Theo – the Neo4j Swift driver. Niklas is a Neo4j Ninja and you can find him on LinkedIn.
GRAPH DATABASE: Importing Data From CSV Into Neo4jIn this video, Tom from Graphileon demonstrates the latest version of their CSV import wizard for Neo4j. With a single CSV file, you’ll create nodes and relationships with their properties, including relationships between the same node types.
INTEGRATION: Your Stack and Neo4jIn this blog, Ben Simpson shares how he integrated Neo4j into a Rails application in an organized, fault-tolerant, and asynchronous way. His organization, a medical professional network, models their network of users as a knowledge graph to determine degrees of connection, as well as other relationships.
KNOWLEDGE GRAPH: Maintain a Companion Plant Graph in Google Sheets and Neo4jIn this post, Sixing demonstrates how to create a graph solution to companion planting – the practice of growing diverse plants in proximity that support each other by nutrient provision, beneficial insect attraction, or pest suppression. He creates a knowledge graph from the available table-based information to gain new insights into the data.
NEO4J LIVE: Graphyx – Alteryx Connector for Neo4j
MACHINE LEARNING: ML for Knowledge Graphs With Neo4j
DEEP LEARNING ON GRAPHS: Integration of DGL and Neo4j DBMS for Social Analysis
TWEET OF THE WEEK: @sonygreenDon’t forget to retweet if you like it!
This is really cool. Benjamin Goosman shows how he uses #GraphXR to visualize outstanding issues in Atlassian Jira and Airtable and inject the data to Neo4j Aura. #jira | #airtable | #confluence | Atlassian | Neo4j | Global Data Geeks https://t.co/aLiezorbLF— Sony Green (@sonygreen) August 30, 2022