Category: Graph Databases

Feed: Cambridge Intelligence. Author: Andrew Disney. Colors can make or break your data visualization.A carefully selected color palette helps you to harness the pre-attentive processing powers of the human brain, and makes insight clearer and easier to find. A badly chosen color palette obscures the information your users need to understand, and makes your data visualization less effective and harder to use.This blog post explores some data visualization examples, basic color theory, and explains how it can help you design visualizations that look good, and make data more compelling.Let’s start with some examples to show you the importance of choosing ... Read More

Feed: Cambridge Intelligence. Author: Dan Williams. In this final post in our three-parter exploring the typical challenges you face when visualizing your graph data for the first time, we look at the ‘starburst’ – a pattern where one heavily connected node dominates the chart.Usual suspect #3: it’s hard to see past a starburstWhere do starbursts come from?In previous posts, we saw how the underlying data source plays a significant role in determining whether you see a chart dominated by snowstorms or hairballs. But in both those cases, the main focus was on visualizing a dataset in its entirety.When working with ... Read More

Feed: Neo4j Graph Database Platform. Author: Mark Needham. Hi graph gang, In this week’s video from the NODES 2019 conference, Pat Brown and Karim Shehadeh explain how they used Neo4j to discover the soul of a product at Under Armour. Nathan Smith finds the optimal solution for Sudoku, Luanne Misquitta teaches us all about Post-Union processing in the newly released Neo4j 4.0, and Adam Cowley builds a real-time UI using Kafka and Vue.js. And finally, I created a QuickGraph of the UK Official Singles Chart. Mark Needham, Karin Wolok, and the Developer Relations team This week’s featured Neo4j Community member ... Read More

Feed: Neo4j Graph Database Platform. Author: David Penick. “Even something as simple as mapping your IT network, which really makes sense when you look at it as a graph, takes the concept of graph and makes it a little bit more tangible,” said Jennifer Reif, Developer Relations Engineer at Neo4j.A graph database like Neo4j is different from what most developers have worked with before. It requires a new perspective and thinking outside the box, but builds on what they already know.In this week’s five-minute interview (conducted at GraphTour DC 2019), Jennifer Reif shares her excitement about spurring aha moments and ... Read More

Feed: Neo4j Graph Database Platform. Author: Corin Gainey. Editor’s Note: This presentation was given by Alexander Jarasch at GraphConnect San Francisco in October 2016.Presentation SummaryAlexander Jarasch is the head of data and knowledge management at the German Center for Diabetes Research in Munich, Germany. In his work, Jarasch looks to integrate the combination of research from a diverse span of independent analysts and use this information to better prevent and treat diabetes.In this presentation, Jarasch chronicles the evolution of diabetes in humans. He briefly answers the question “what is diabetes?” and explains the objectives of the German Center for Diabetes ... Read More

Feed: Cambridge Intelligence. Author: Andrew Disney. If you want to uncover the most influential, well-connected or important individuals in a network, you should turn to social network analysis centrality measures. These graph analysis algorithms are designed to unpick complex networks and reveal the patterns buried in the connections between nodes.In this blog post, we’ll take a look at two centrality measures in our graph visualization toolkits: EigenCentrality and PageRank.How do they work? When should you use them? Read on to find out.EigenCentrality: understand network influenceEigenCentrality measures a node’s influence. It starts by measuring each nodes ‘degree’ score – which is ... Read More

Feed: Cambridge Intelligence. Author: Gizem Unal. As a marketing campaign & automation manager, I spend a lot of time analyzing data from multiple sources to find out what attracts people to our website. Whether it’s a tweet, an email or a Google advert, those who kindly give their consent for tracking marketing preferences help us understand what’s performing well.Analyzing this data gives me some idea of the problems facing our customers, particularly those working to understand customer 360 business insight and ‘know your customer’ regulatory compliance. What’s the best way to find out how your customers engage with your brand, ... Read More

Feed: Neo4j Graph Database Platform. Author: Mark Needham. Hi graph gang, In this week’s video from the NODES 2019 conference, Joe Depeau explains how graph algorithms through the medium of classic teen films. Nathan Smith tries to solve a Sudoku, Jesús Barrasa graphs Christmas messages, and Mike Solomon analyses Twitter data. And finally, Greg Shackles uses F# and Neo4j to analyse a .NET dependency graph. Enjoy! Mark Needham, Karin Wolok, and the Developer Relations team This week’s featured Neo4j Community member is Michael Porter, Founder at Muddy Boots Code. Michael Porter – This Week’s Featured Community Member Did we mention ... Read More

Feed: Neo4j Graph Database Platform. Author: Allison Wu. Welcome to this week’s #GraphCast – our series featuring what you might have missed in Neo4j media from the past fortnight.Last time, our Editor-in-Chief, Bryce Merkl Sasaki, showcased our newly launched Graphistania 2.0 podcast. It highlights cutting-edge projects and developments in the Neo4j community. This week, I’ve chosen to feature our How-To Video Series, which aims to help our graphistas who are just getting started with Neo4j development.Check out one of our recent episodes below to learn more about creating a database with Neo4j Aura.This series covers all the bases (pun intended), ... Read More

Feed: Blog – ArangoDB. Author: Bogdan Udubasa. Nothing performs faster than arangoimport and arangorestore for bulk loading or massive inserts into ArangoDB. However, if you need to do additional processing on each row inserted, this blog will help with that type of functionality. If the data source is a streaming solution (such as Kafka, Spark, Flink, etc), where there is a need to transform data before inserting into ArangoDB, this solution will provide insight into that scenario as well. Let’s delve into the specifics. ArangoDB is version v3.6, and we will assume that it has already been installed (I am ... Read More