QPython User Guide - Module 2: Quantexa Knowledge Graph — Now Available
We're excited to launch the QPython User Guide - Module 2: Quantexa Knowledge Graph on the Community — our second QPython-based module, following the release of Processing Quantexa Assets in February 2026. Target Audience This User Guide is designed for: Data Scientists Data Analysts Anyone working with Quantexa Batch Resolver outputs who wants to explore and apply the full capabilities of the Quantexa Knowledge Graph Technical Requirements To get started, you will need: A Quantexa license to run Knowledge Graphs Access to Quantexa Batch Resolver output Access to QPython and Quantexa Knowledge Graph libraries Working knowledge of Python (no Scala required) Program Format This is a Community-based User Guide, available to all registered members. Throughout the Guide, you’ll work hands-on with core capabilities of the Quantexa Knowledge Graph. This includes leveraging Batch Resolver outputs, exploring graph data interactively, and creating new relationships between nodes and edges through Analytical Perspectives. You’ll also be introduced to scalable graph analytics, such as PageRank, and learn how to export your outputs into widely used open-source formats, including Parquet, NetworkX, SciPy, GraphFrames, and PyTorch. Get Started You can jump straight in - no enrolment or VDI is required, and all content is accessible directly through the Community Platform. Explore Now! Duration We recommend allowing 2–3 days (full-time) to work through the complete set of lessons and hands-on examples. Program Structure The User Guide is organized into four lessons, designed to be completed sequentially. 1. The Conceptual Foundations of the Quantexa Knowledge Graph Understand the principles and concepts that underpin the Quantexa Knowledge Graph. 2. The Quantexa Knowledge Graph Pipeline Learn how to construct and manage a Knowledge Graph. 3. Ego-centric Graph Analytics Explore graph data from a localized, neighborhood perspective. 4. Population-level Graph Analytics Scale your analysis across the full graph. Questions or Support If you have any questions about the content or how to get started: Reach out via the Community discussion channels, or Contact the Training Team (Education Services) directly. We hope you enjoy the program We’re excited to make this content available and hope you find it valuable as you continue building your expertise with the Quantexa Knowledge Graph. Your feedback plays an important role in helping us improve, so we encourage you to share your thoughts and experiences as you work through the Guide.
Using QPython for Analytics and Data Science Teams
Harnessing the full potential of your data is critical to driving impactful insights and innovation. QPython, Quantexa’s Python SDK, empowers data scientists to build cutting-edge ML and AI models by leveraging Quantexa’s robust Network features and Knowledge Graphs. In this guide, we’ll walk you through extracting and flattening documents and entities from Parquet files, re-ingesting model outputs into Quantexa, and implementing QPython in your workflows. Whether you're new to QPython or looking to refine your expertise, this article will equip you with the knowledge to deploy it effectively and elevate your data science initiatives. See the full guide (login required): Using QPython for Analytics and Data Science Teams - Quantexa Community Harnessing the full potential of your data is critical to driving impactful insights and innovation. QPython, Quantexa’s Python SDK, empowers data scientists to build cutting-edge ML and AI models by leveraging Quantexa’s robust Network features and Knowledge Graphs. In this guide, we’ll walk you through extracting and…
Welcome to Quantexa 2.5 | 2.5.0 Release Announcement
We are pleased to announce Quantexa 2.5! Read below for a few exciting highlights. Stream Updates to the Entity Store The Entity Store can now be updated with a streamed ingest of new Documents, and creates a change log describing how the Entity population has changed. In combination with the ability to query the full population of resolved Entities, this update enables near real-time access to the most up-to-date view of your data. Why is this important? Users and downstream systems can now get the latest view of an Entity resolved by Quantexa, enabling business processes that need to access or act upon that update in near real-time - in a Master Data Management solution (MDM), for example - to do so. Deploy and Change Custom Scoring Pipelines through Configuration The addition of Scoring Extension Mode to the Assess Template Generation functionality helps simplify the process of setting up Scoring on a project. With the new Extension Mode, it is now possible to change and add new nodes to a custom Scoring model using configuration files. Why is this important? This significantly reduces the technical skills required for setting up the Scoring pipeline and makes the generation of a custom Scoring pipeline more flexible. Flexible Scorecards: Identify and Aggregate Insights Across Multiple Typologies Assess capabilities and helper functions have been added to support multiple Scorecards on the same level within one Scoring pipeline. Scores can contribute to one or more Scorecards and flexible alerting logic can be set up to include the outcome of multiple Scorecards. Why is this important? This enables the flagging of different types of risks or insights at the same level - for different typologies or different products, for example - and helps an investigator or analyst understand and act upon the full context of the data available. Tune Scorecards with QPython QPython utilities can now interact with Assess configuration files and Scoring pipelines, which simplifies the process of tuning a Scoring model, providing an easy way to conduct a what-if analysis. The interface, using pre-written Python Notebooks, calculates a set of the most common and useful metrics for tuning. The use of Jupyter Notebooks allows the export of the reports for inclusion in the wider model governance process. Why is this important? Scorecard tuning is one of the most important parts of model development, as the model must output the most relevant (e.g., risky or interesting) information for a specific use case. These utilities make the process of Scorecard tuning much more accessible, simple, and quick. It also makes sure that the model addresses the business need and risk appetite of the deployment. Other highlights Speed up and simplify deployment of Explorer with no-code configuration; Merge and split Entities, and override Entity Attributes through the Entity Management Panel (e.g., for Master Data Management solutions); Integrate with Kafka streaming typologies more easily. Share your thoughts on this release in our poll: Which feature of Quantexa 2.5 are you most excited for? To receive updates on every release, be sure to follow the Release Announcements topic: Want to learn more? Check out the Release Notes on the Quantexa Documentation site. If you are unable to access the Documentation site, please get in touch with your Quantexa point of contact or the Community team at [email protected].
Network Analysis of ICIJ data shows how to stop Russian oligarchs in their tracks
I thought this was really cool and wanted to share. Complex systems of secrecy: the offshore networks of oligarchs: and to really rein in Putin’s allies’ wealth, governments should target their financial enablers, a new study suggests, read more here: Analysis of ICIJ data shows how to stop Russian oligarchs in their tracks - ICIJ To really rein in Putin’s allies’ wealth, governments should target their financial enablers, a new study suggests.
