Fraud Detection Using Graph Technology

Graph technology refers to the storage, management and querying of data graphical representation. Here, your indices become vertices and your relationships are converted into edges. Through analysis of the fine-grained relationships, by using graph analysis, you can find out oddities… Read more →

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Graph Algorithms in Neo4j: Use Cases for Graph Transactions & Analytics

Today’s most pressing data challenges center around connections, not just tabulating discrete data. Graph analytics accelerate breakthroughs across industries with more intelligent solutions. This blog series is designed to help you better leverage graph analytics so you can effectively innovate… Read more →

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How to Know What You Know:
5-Minute Interview with Dr. Alessandro Negro, Chief Scientist at GraphAware

“I want to know what I know. That describes what knowledge graphs do for companies,” said Dr. Alessandro Negro, Chief Scientist at GraphAware. In this week’s five-minute interview, we discuss how GraphAware uses natural language processing to help companies gain… Read more →

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Graphs4Good: Connected Data for a Better World

You’re reading this because of a napkin. It was the year 2000, and I was on a flight to Mumbai. Peter, Johan and I had been building an enterprise content management system (ECM) but kept running up against the challenge… Read more →

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Graph Databases for Beginners: Graph Theory & Predictive Modeling

There’s a common one-liner, “I hate math…but I love counting money.” Except for total and complete nerds, a lot of people didn’t like mathematics while growing up. In fact, of all school subjects, it’s the most consistently derided in pop… Read more →

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Graph Databases for Beginners: Why a Database Query Language Matters (More Than You Think)

Languages (the natural, human kind) shape how you view the world. From color to time to gender relations, there’s no escaping how language limits (or expands) your worldview. Words are the categories and labels that we use to process and… Read more →

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Democratizing Data Discovery at Airbnb

Editor’s Note: This presentation was given by John Bodley and Chris Williams at GraphConnect Europe in May 2017. Presentation Summary Airbnb, the online marketplace and hospitality service for people to lease or rent short-term lodging, generates many data points, which… Read more →

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Graph Databases for Beginners: Data Modeling Pitfalls to Avoid

With the advent of graph database technology, data modeling has become accessible to masses. Mapping business needs into a well-defined structure for data storage and organization has made a sortie du temple (of sorts) from the realm of the well-educated… Read more →

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Knowledge Graphs: The Path to Enterprise AI

Editor’s Note: This presentation was given by Michael Moore and Omar Azhar at GraphConnect New York in October 2017. Presentation Summary Once your data is connected in a graph, it’s easy to leverage it as a knowledge graph. To create… Read more →

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The ROI on Connected Data:
Extract More Value from Existing Data Using Graphs [+ Telia Case Study]

From LinkedIn to Facebook to Google, top companies are driving their businesses using graphs. And use cases span industries as well. What those new to graph technology may not understand is that creating a graph does not require starting from… Read more →

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Graph Algorithms in Neo4j:
15 Different Graph Algorithms & What They Do

Graph analytics have value only if you have the skills to use them and if they can quickly provide the insights you need. Therefore, the best graph algorithms are easy to use, fast to execute and produce powerful results. Neo4j… Read more →

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Neo4j as a Critical Aspect of Human Capital Management (HCM)

Editor’s Note: This presentation was given by Luanne Misquitta at GraphConnect Europe in May 2017. Presentation Summary In this presentation, Luanne Misquitta shares her facility for engaging with the challenges of human capital management (HCM) in contemporary organizations using graph… Read more →

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Neo4j: A Reasonable RDF Graph Database & Reasoning Engine [Community Post]

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] It is widely known that Neo4j is able to load and write RDF. Until now, RDF… Read more →

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Network Science: The Hidden Field behind Machine Learning, Economics and Genetics That You’ve (Probably) Never Heard of – An Interview with Dr. Aaron Clauset [Part 2]

Last week, in part one of my interview with Dr. Aaron Clauset, we reviewed how network science was evolving and how it’s dismantling preconceived notions about networks in general. Clauset also stressed the crucial role of interdisciplinary collaboration when it… Read more →

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Network Science: The Hidden Field behind Machine Learning, Economics and Genetics That You’ve (Probably) Never Heard of – An Interview with Dr. Aaron Clauset [Part 1]

I recently had the opportunity to combine work and pleasure and meet with Dr. Aaron Clauset, an expert on network science, data science and complex systems. In 2016, Clauset won the Erdos-Renyi Prize in Network Science but you might be… Read more →

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How to Import the Bitcoin Blockchain into Neo4j [Community Post]

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] This guide runs through the basic steps for importing the bitcoin blockchain into a Neo4j graph… Read more →

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The ICIJ Releases Neo4j Desktop Download of Paradise Papers

Early this morning (1 Dec.), the Pulitzer Prize-winning International Consortium for Investigative Journalists (ICIJ) released an ICIJ version of Neo4j Desktop which includes the Paradise Papers and the other Offshore Leaks graph data. This desktop package – available for Windows,… Read more →

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Analyzing the Paradise Papers with Neo4j: A Closer Look at Queries, Data Models & More

Our friends from the ICIJ (International Consortium of Investigative Journalists) just announced the Paradise Papers this past week, a new trove of leaked documents from the law firm Appleby and trust company Asiaciti. Similar to the Panama Papers before (which… Read more →

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Neo4j: The Power behind the Paradise Papers

Once again, the International Consortium of Investigative Journalists (ICIJ) has shaken the world with a far-reaching, in-depth investigation into the shadowy world of offshore finance: The Paradise Papers. Using Neo4j, the ICIJ has built upon their Pulitzer Prize-winning investigation of… Read more →

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Going Meta: Exploring the Neo4j Graph Database…as a Graph

The graph data model is inherently visual. Try explaining a graph to someone new. You’ll inevitably draw a picture, or wave your hands around to convey what you mean by ‘nodes… links…. and more nodes’. People think in graphs, and… Read more →

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Analyzing Twitter Hashtag Impact using Neo4j, Python & JavaScript

This is the first demo I developed with Neo4j. The objective of the demo is to open the discussion about graph databases, Neo4j, big data, analytics and IBM Power Systems with our global customers. I decided to use Twitter as… Read more →

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Graph Algorithms: Make Election Data Great Again

Editor’s Note: This presentation was given by John Swain at GraphConnect San Francisco in October 2016. Summary In this presentation, learn how John Swain of Right Relevance (and Microsoft Azure) set out to analyze Twitter conversations around both Brexit and… Read more →

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A Game of Data and GraphQL [Game of Thrones Community Post]

This blog post was originally published on Medium by Michael Hunger and is used with permission. Creating a Neo4j graph database (and more) based on Game of Thrones (and A Song of Ice and Fire) data. As season 7 is… Read more →

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The Top 13 Resources for Understanding Graph Theory & Algorithms

Recently we announced the availability of some super efficient graph algorithms for Neo4j. In case you missed the announcement, we now have an easy-to-use library of graph algorithms that are tuned to make full use of compute resources. As part… Read more →

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Machine Learning, Graphs and the Fake News Epidemic (Part 2)

In last week’s post, we discussed why designing a fully automated fake news detector is currently infeasible and introduced a semi-automated, graph-based solution which would use machine learning to work alongside human fact checkers to scalably flag and quarantine fake… Read more →

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