Announcing NODES 2022! Submit Your Talk
Aug 09 2 mins read
NODES 2022 is a free, multi-track, two-day virtual conference of highly technical presentations by developers and data scientists solving problems with graphs. Read more →
User Segmentation Based on Node Roles in the Peer-to-Peer Payment Network
Jul 21 12 mins read
Knowing your users is vital to any business. When your users can interact with each other on a social media platform, content sharing platform, or even work-related platforms, you can construct a network between your users based on their interactions and extract graph-based features to segment your users. Of course, these same approaches can be applied to other platforms that are not user-centric. Read more →
Using Neo4j Graph Data Science in Python to Improve Machine Learning Models
Jul 06 14 mins read
A wave of graph-based approaches to data science and machine learning is rising. We live in an era where the exponential growth of graph technology is predicted [1]. The ability to analyze data points through the context of their relationships… Read more →
algorithmsdata-sciencefraudgraphgraph data sciencegraph-algorithmsmachine-learningNeo4jNeo4j Graph Data SciencepythonUsing Neo4j Graph Data Science in Python to Improve Machine Learning Models
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 4
Mar 01 9 mins read
In part 4 of our fraud detection series, we will cover how to apply graph machine learning to predict the high fraud risk user accounts we labeled in parts 1, 2, and 3. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 3
Mar 01 5 mins read
In part 3 of our fraud detection series, we may want to expand beyond our business logic to automatically identify other users that are suspiciously similar to the fraud risks already identified. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 2
Mar 01 6 mins read
In part 2 of this fraud detection series, we will provide more formal definitions for resolving entities that will allow us to partition well-defined communities in a scalable manner. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 1
Mar 01 6 mins read
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. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Summary
Mar 01 3 mins read
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. Read more →
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