This blog post deals with solving fraud detection problems with graph machine learning. Learn how to load data, trainin and plot to find out who did it! It’s elementary, my dear reader.
The new version of Memgraph’s open-source graph extension library, MAGE, now supports node classification and link prediction algorithms. Install the new version of MAGE if you would like to write custom algorithms faster by using the C++ API, need the igraph algorithms or k-means clustering.
Graph Neural Networks can be used for a variety of applications but do you know what it takes to create a great recommendation system? Dive deep into the math of GNNs, implement a link prediction module and show everyone how stunning graph machine learning can be!
Data lineage helps you make informed decisions that reduce costs, streamline operations and power innovation. Discover how stream tech helps with automatically mapping data lineage, and learn how Memgraph integrates with event streaming platforms.
The data lineage graph is the single source of truth about your organization’s data. Discover how Memgraph can competently handle this use case with its optimized architecture, power data insights and connect to other software.
For every problem in the energy management system, there is a graph algorithm that can point you in the right direction! Here is an overview of the most useful graph algorithms for highlighting weak links, high-risk nodes and many more.
It’s true every recommendation engine requires a performant database to analyze the data and provide the recommendation, but why exactly does Memgraph stand out? Easy - C++, in-memory, real-time analytics! Three things to change the recommendation game.
If you require an energy management system that is scalable, fault-tolerant, and performant, Memgraph is the go-to solution! Analyze highly connected power grids or gas pipelines to make meaningful decisions and improve the impact on your business, the people and the environment around you.
When you notice your traditional IAM system no longer provides adequate analysis and decision making is getting harder as your company grows because you always have to pick up the slack manually, it’s high-time you turn your attention to graphs. They have everything you need - high performance, flexibility and scalability.
The GDPR has placed high demands on organizations doing business in the European Union, mainly focused on how personal data is collected and processed. However, this does not mean it can’t be business as usual again. Find out why graph databases are the best way to achieve GDPR compliance and how they get it done.
Are you reluctant to switch from a relational database to a graph databases to explore fraud because you believe you first need to be proficient in Cypher to correctly import the data? Be rest assured - there is a Python-friendly approach available within Memgraph!
Antonio, also known as Fico, joined Memgraph almost three years ago! He started as a student in the Cloud team, but his career path changed as his interest increased. Without further ado, let’s get straight to the point and find out more about Fico’s background, career path, and role in the interview below.
With power being the most powerful asset, it’s still managed by inadequate tools and systems based on tabular data. Good for aggregations and mathematical operations but terrible for actually managing large-scale, highly connected dynamic systems. Luckily, graphs can regain control over energy systems and topologies, and help save millions.
If you are spending more time writing code to develop, deploy and manage your graph projects, it’s time you tried Memgraph. It will allow you to focus on the data analysis and free you from all that time-consuming coding.
If your data is trapped inside tables and you can’t seem to get satisfying answers to questions that would enhance your business, it’s time to switch to graph databases. Here are three main reasons why!
The world has changed a lot in the past couple of years, and it’s no different for business organizations. More and more businesses no longer have strict hierarchical organizations and people often change teams and projects they work on and resources they need. It is no wonder that if the IAM systems also don’t change, they will no longer be helpful in supporting the organization. Switching to graphs presents a change the IAM systems desperately need.
You no longer need to rely on manually inspecting data lineage before making changes to your organization’s data landscape. Find out how to get insights with Memgraph’s analytics so that you can move on to impact analysis, data migration, or upgrading your data infrastructure!
Are your NetworkX algorithms taking even more and more time to produce the results you need to finish up your research? Or the application reached a critical point and its starting to lag due to increase in data analysis? Could Memgraph tackle the same computations in less time? I think you probably know the answer is “Doh!” but here are the numbers to prove it.
If a recommendation engine built on relational databases is falling a part due to the bottlenecks made by complex JOINs and never-ending schema changes, there is only one permanent and game changing solution - graph databases.
Complex JOINs necessary for tracking data lineage with relational DBs drag down the speed of analyzing and visualizing the lineage and pinpointing issues and solutions. That is why graph technology is perfect to model and manage data lineage! Not convinced? Read the post to find out more.
When NetworkX can no longer handle the analysis and vizualisation requirements of your project, and you are tired of constantly reloading data, find out how you can utilize Memgraph to get your graph data analysis back on track.
Sometimes choosing graphs solutions isn’t the only step you can take to mitigate risks. In case fraudsters foolishly think they can outsmart an analytics team, they probably haven’t come across their new best friend - fraud detection systems enhanced with machine learning models.