With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
Fabio Montagna is Lead Machine Learning Engineer at GraphAware and presented Temporal Graph Analysis at NODES2022. In this session, we’ll share our experience with horizon scanning over a graph of medical research papers. By leveraging the author keywords from scientific publications, it’s possible to build a cooccurrence graph with a temporal component provided by the paper publication date. We’ll show how we can analyze trends and evolution patterns using an unsupervised algorithm that assigns roles to author keyword.
Christophe Willemsen, CTO at GraphAware, spoke on NODES2022 about using Neo4j with Docker and Docker Compose, presented tips and tricks on basic usage, gave an explanation of the Docker image itself, backups and restore and building custom images extending the official Neo4j image.
Federica Ventruto and Alessia Melania Lonoce are Junior Data Scientists at GraphAware who spoke at NODES2022. Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Vlasta Kůs is Lead Data Scientist at GraphAware and presented at NODES2022. Public archives contain incredible amount of knowledge. In this session, we’ll cover a real use case of building a knowledge graph for the archive of a major foundation to help empower researchers (or business analysts) to access previously unavailable levels of insights. This archive, going up to a century back, contains detailed information about funded projects and conversations preceding them, budgets, research endeavors, and outcomes, as well as priceless knowledge about influence networks of foundation representatives, researchers, and students. A particular challenge was that the same events were described in multiple sources. The only way to leverage all of this knowledge was through the use of advanced analytics and machine learning. We will explore the technologies (including OCR, NLP, and graph data science) and complex pipelines employed to create this major knowledge graph.
Estelle Scifo is a Machine Learning Engineer at GraphAware and presented at NODES2022. Leverage Cypher map projections and Python dynamic typing to build an Object Graph Mapper for Neo4j. In this step-by-step session, you’ll learn how to get started on such a project, from defining the framework API to automatically building Cypher queries.
With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
Graphs can be truly transformational for law enforcement agencies. Learn how a cutting-edge graph solution removes obstacles from the criminal intelligence process and increases its efficiency.
A Fortune 500 Retailer saved $6M using Hume to prevent scams. The soft production was deployed in 3 months, and within 6 months it exceeded the scam detection KPIs by 300%.
Hume provides a single view of intelligence enabling analysts to quickly identify links and patterns of interest in the vast amounts of information they have access to. On a single canvas, analysts quickly find connections between entities, understand geographical and temporal context, and perform advanced network analysis.
Learn more about how Law enforcement agencies are already using Hume to power their intelligence analysis and achieve success.
Hume is a mission-critical graph analytics solution that allows analysts in financial institutions to easily visualise and monitor complex flows of money and detect patterns of suspicious activities.
Learn more about how to quickly act to disrupt fraudulent behaviour and protect your clients and your business.
