Blog Contributor

Predictive modeling is one of the most fundamental tasks of a data scientist, and you’ll encounter it in nearly every job and industry in the field. Keeping your knowledge and skills up-to-date is essential to driving efficiencies and revenue at your company. In this article, you'll... read more

With the immense amount of data being generated daily, organizations are drawn to advanced analytics, data science, machine learning, and AI to drive better forecasting, more accurate predictions, and truly novel innovations. But many businesses fail to reap these benefits. Instead, they... read more

Graph Neural Networks (GNNs) are gaining tons of recognition in the machine learning community due to their potential for solving complex tasks in social networks, drug discovery, recommendation systems, and more. Unlike traditional neural networks that operate on fixed-size, ordered... read more

Using Neo4j Graph Data Science to Understand and Improve Your Supply Chain Performance In this post we demonstrate how Neo4j Graph Data Science can be applied to supply chain data to: Find new, faster, paths through supply chain processes with potential to improve performance by up to... read more

In part 1 of this series, we demonstrated how supply chain data can be modeled into a graph, imported into Neo4j, and analyzed using Graph Data Science (GDS). We walked through how to visualize the supply chain in Bloom and used the new GDS integration to understand operational load, flow... read more

Actionable insights in minutes, using Neo4j Graph Data Science and Bloom to intuitively visualize and extract supply chain insights around operational load, flow control, and regional patterns. Supply chains are inherently complex, involving multiple stages, inputs, outputs, and... read more

In the three previous parts of this series, we explored the graph, identified new fraud risk accounts and communities, and covered techniques to recommend new suspicious users. In this section, we will cover how to apply graph machine learning to predict the high fraud risk user accounts we labeled... read more

In parts 1 & 2 of this series, we explored the graph and identified high risk fraud communities. At this stage, we may want to expand beyond our business logic to automatically identify other users that are suspiciously similar to the fraud risks already identified. Neo4j and GDS makes it... read more

Identifying communities that reflect underlying groups of individuals is often a key step to fraud detection. In part 1 of this series, we explored with Louvain. In part 2, we will provide more formal definitions for resolving entities that will allow us to partition well-defined communities in a... read more

In the first part of this fraud detection series, we will introduce the sample graph dataset we are using and begin exploring the graph for potential fraud patterns.The technical resources to reproduce this analysis and the analysis in all subsequent parts of this series are contained in this... read more

Fraud Detection is one of today’s most challenging data science problems. Thankfully, Neo4j Graph Data Science (GDS) offers practical solutions that empower data scientists to make rapid progress in fraud detection analytics and machine learning. SummaryWhether you are responsible for... read more

In this post we explore how to get started with practical and scalable recommendation in graph. We will walk through a fundamental example with news recommendation on a dataset containing 17.5 million click events and around 750K users. We will leverage Neo4j and the Graph Data Science (GDS)... read more

Photo by Alina Grubnyak on UnsplashWhile Supervised Entity Resolution (ER) can be immensely valuable, it is sometimes difficult to apply and scale in the real-world enterprise setting.In this post, I explore how the Neo4j Graph Data Science (GDS) library can be applied to rapidly develop... read more