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.
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!
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 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!
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.
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.
Movie ratings from MovieLens are incoming, but you are still not sure what to watch over the weekend? Create your own movie recommendation system.
A Knowledge Graph is a reusable data layer that is used to answer sophisticated queries across multiple data silos. With contextualized data displayed and organized in the form of tables and graphs, they achieve pinnacle connectivity.
Temporal graph neural networks can be used to perform both label classification and link prediction. Learn how to create a simple graph recommendation engine using TGNs on an Amazon product dataset.