Identify Patterns and Anomalies With Community Detection Graph Algorithm

Betweenness centrality is one of many measures you can get from performing a centrality analysis of your data. It identifies important entities in your network that are actually a vulnerability and can bring your processes to a standstill. Dive deeper into this important metric and how it can be used in various use cases.

Hacker News is a website that contains content from the tech industry and to find yourself among its most popular posters sometimes seems a miracle! To break the mystery around it, I tried to knowledge-hack it with Kafka and PageRank algorithm - read on to find out what I discovered!

If you need to reap the benefits of both a graph database and Elasticsearch, the new module in Memgraph’s graph library MAGE enables you to easily synchronize those two components using triggers.

As a vast number of use cases in cybersecurity involves network-like representation of data, we outline why Memgraph is the best graph database for you in terms of performance, analytics and visualizations.

What is PageRank algorithm? How can it be used in various graph database use cases? How to use it in Memgraph? If these questions are keeping you up at night, here is a blog post that will finally put your mind at ease.

People tend to update versions of their code project dependencies without inspecting security impacts on their code. In this article, we use Memgraph to analyze Python package vulnerabilities when updating dependencies and provide you with a performant solution using known and reported vulnerabilities.

With the rising number of cyber-attacks followed by the massive digitalization of companies, the right tool is needed to maximize performance and prevent further attacks from happening. We explain why graph databases offer a perfect choice in cybersecurity use cases and why they make your business more secure.

The Conference on Innovative Data Systems Research (CIDR) is a systems-oriented conference organized every two years since 2003. Check out what interesting people, talks and papers made Memgraph’s CTO Marko excited about the future of graphs.

The number one reason for creating a knowledge graph is to find the knowledge not visible at first glance. The simplest way to discover new knowledge using a graph database is by matching patterns. Finding new patterns can help you discover fraudulent activities or discover alternative action for guaranteed success.

Find out how you can minimize decision-making risks when dealing with networks by using Memgraph as the one-and-only tool for a complete analysis.

Little by little, the hosting cost of a graph database can turn out to be quite substantial. Hosting costs are highly correlated with how much resources a database uses, which is not as straightforward as you might think. Find out how expensive it is to host Memgraph and Neo4j instances and why.

Optimizing a supply chain network can get really messy if you can’t identify dependant products, correctly schedule processes and find critical points in the pipeline. With Memgraph, you can accelerate your supply chain pipeline and build a complete analysis tool to increase the shipments of your goods.

When choosing a graph database, you are probably thinking about the costs of certain features your solution requires. The more production-ready the community edition is, the fewer costs you will have! If you are choosing between Memgraph and Neo4j, both open-source databases, check how community editions compare and find out why Memgraph is a feature-rich community edition ready for production.

Making decisions that point your business in the right direction is much easier with knowledge graphs. But, to create a knowledge graph, you need to gather all the data scattered in different silos, analyze the current connections between data points and discover new connections. It’s a complex task, but graph databases, such as Memgraph, make it a manageable one.

Building real-time analytical applications require capable infrastructure. Picking the right software infrastructure components can take time and effort. When it comes to graph databases, find out why Memgraph is a fast and powerful real-time graph database.

Read about our seventh Graph Data Zagreb meetup and what topics were covered.

As networks consist of highly connected data, with Memgraph’s in-memory storage you can analyze network topologies quickly to gain insights from static or real-time data. Discover critical points in the network or component dependencies, optimize resources and run what-if scenarios, then present those findings visually to extract every last bit of information.

One issue many companies face today is that they have a lot of siloed data, making it difficult to draw conclusions or reason about the processes that drive their business. By using graph technology, it is easy to create knowledge graphs and use this data to gain insights and make informed decisions.

Start next year with a whole new range of features and graph algorithms to gain insights that will make it a happy year indeed! Developed features include improved security, benchmark tests, projected graph visualization, better code suggestion support, C++ API, node classification and link prediction, to name just a few.

With the Introduction to graph analytics with Python course, you will learn all about graphs and how to analyze them. Check out the overview of the graph analytics tools landscape and engaging examples to find out how to use the most powerful network analysis Python tools.

Although networks are an easy concepts to understand, they are poorly managed in many various industries. Learn how graphs can help scale your network topologies and draw conclusions crucial for your business

A lot of companies today have massive amounts of siloed data just sitting there and not being used. No information or knowledge was gained, and no conclusions were made. For the data to be useful, it needs to be interconnected and shaped into a knowledge graph that will produce value for the company. Read how graphs can help!

This blog post deals with solving fraud detection problems with graph machine learning. Learn how to load data, training 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.

Check out how Memgraph handles the visualization of complex graph structures found in insurance data that will allow you to comprehend and synthesize large amounts of new information more efficiently.

Memgraph delivers results up to 120 times faster than Neo4j while consuming one quarter of the memory!

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.

Find out how to organize data into a graph. Model vehicle insurance claims, incidents, policies and more while preserving the interconnected nature of individual insurance cases.