The document discusses graph data science techniques in Neo4j. It provides an overview of graph algorithms categories including pathfinding and search, centrality and importance, community detection, similarity, heuristic link prediction, and node embeddings and machine learning. It also summarizes 60+ graph data science techniques available in Neo4j across these categories and how they can be accessed and deployed. Finally, it discusses graph embeddings and graph native machine learning in Neo4j, covering techniques like Node2Vec, GraphSAGE, and FastRP.
The Neo4j Data Platform for Today & Tomorrow.pdfNeo4j
The document discusses the Neo4j graph data platform. It highlights that connected data is growing exponentially and graphs are well-suited to model real-world relationships. Neo4j provides a native graph database, tools, and services to store, query, and analyze graph data. Key capabilities include high performance, flexible schemas, developer productivity, and supporting transactions and analytics workloads.
The document discusses knowledge graphs and their value for organizations. It notes that two-thirds of Neo4j customers have implemented knowledge graphs and that 88% of CXOs believe knowledge graphs will significantly improve business outcomes. Knowledge graphs are described as interconnected datasets enriched with meaning to enable complex decision-making. Examples of how knowledge graphs have helped companies with recommendations, fraud detection, and track and trace are provided.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
This document discusses how knowledge graphs can enhance general artificial intelligence systems by grounding them with facts and contextual information. It presents several use cases for combining knowledge graphs with large language models, including generating personalized natural language experiences, powering natural language search across both explicit and implicit relationships, and constructing knowledge graphs from unstructured text. The document also demonstrates how an LLM can extract entities and relationships from a medical case study and represent them in a knowledge graph for further querying.
Boost Your Neo4j with User-Defined ProceduresNeo4j
The document discusses user-defined procedures and functions in Neo4j. It begins with an example of a simple "Hello World" user-defined function. It describes how procedures and functions can be written in any JVM language, deployed to the database, and accessed via Cypher. It provides examples of real-world uses like optimizing queries for category overlap. It also discusses existing libraries like APOC that provide common graph algorithms and functions. The document provides guidance on developing, testing, and deploying custom procedures and functions.
Scaling into Billions of Nodes and Relationships with Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j Graph Data Science (GDS) and its ability to scale to billions of nodes and relationships. It outlines a typical GDS workflow involving graph projection, algorithm execution, and data export. It then discusses challenges of scaling GDS, including data size, import/export speeds, and algorithm performance. The document dives into how GDS addresses these challenges through techniques like graph compression, parallel processing, and optimized data structures like "huge collections" to handle large primitive data types in Java.
This document discusses knowledge graphs and how they can be used to drive intelligence into data. It describes how knowledge graphs can organize different types of data relationships and enable applications such as semantic search, pattern matching, and analyzing dependencies. Specific use cases are provided for skills discovery, root cause analysis, and military equipment management. Knowledge graphs are presented as a way to bridge data silos and enable a unified data fabric.
The Neo4j Data Platform for Today & Tomorrow.pdfNeo4j
The document discusses the Neo4j graph data platform. It highlights that connected data is growing exponentially and graphs are well-suited to model real-world relationships. Neo4j provides a native graph database, tools, and services to store, query, and analyze graph data. Key capabilities include high performance, flexible schemas, developer productivity, and supporting transactions and analytics workloads.
The document discusses knowledge graphs and their value for organizations. It notes that two-thirds of Neo4j customers have implemented knowledge graphs and that 88% of CXOs believe knowledge graphs will significantly improve business outcomes. Knowledge graphs are described as interconnected datasets enriched with meaning to enable complex decision-making. Examples of how knowledge graphs have helped companies with recommendations, fraud detection, and track and trace are provided.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
This document discusses how knowledge graphs can enhance general artificial intelligence systems by grounding them with facts and contextual information. It presents several use cases for combining knowledge graphs with large language models, including generating personalized natural language experiences, powering natural language search across both explicit and implicit relationships, and constructing knowledge graphs from unstructured text. The document also demonstrates how an LLM can extract entities and relationships from a medical case study and represent them in a knowledge graph for further querying.
Boost Your Neo4j with User-Defined ProceduresNeo4j
The document discusses user-defined procedures and functions in Neo4j. It begins with an example of a simple "Hello World" user-defined function. It describes how procedures and functions can be written in any JVM language, deployed to the database, and accessed via Cypher. It provides examples of real-world uses like optimizing queries for category overlap. It also discusses existing libraries like APOC that provide common graph algorithms and functions. The document provides guidance on developing, testing, and deploying custom procedures and functions.
Scaling into Billions of Nodes and Relationships with Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j Graph Data Science (GDS) and its ability to scale to billions of nodes and relationships. It outlines a typical GDS workflow involving graph projection, algorithm execution, and data export. It then discusses challenges of scaling GDS, including data size, import/export speeds, and algorithm performance. The document dives into how GDS addresses these challenges through techniques like graph compression, parallel processing, and optimized data structures like "huge collections" to handle large primitive data types in Java.
This document discusses knowledge graphs and how they can be used to drive intelligence into data. It describes how knowledge graphs can organize different types of data relationships and enable applications such as semantic search, pattern matching, and analyzing dependencies. Specific use cases are provided for skills discovery, root cause analysis, and military equipment management. Knowledge graphs are presented as a way to bridge data silos and enable a unified data fabric.
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...Neo4j
The document discusses how knowledge graphs and graph data science can provide more context and enable better predictions. It provides examples of using knowledge graphs for interactive browsing of patent and pathway data, cross-species ontology graph queries, identifying relevant COVID-19 genes using graph algorithms, and sub-phenotyping patient populations using graph embeddings. The key message is that knowledge graphs harness relationships to provide deep, dynamic context for analytics and machine learning.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j
This document provides an overview of the Neo4j graph data platform and its capabilities for data science and analytics. It discusses Neo4j's native graph architecture, tools for data scientists and analysts, and how Neo4j enables graph data science across the machine learning lifecycle from feature engineering to model deployment. Algorithms, embeddings, and machine learning pipelines in Neo4j are highlighted. Integration with common data ecosystems is also covered.
The Data Platform for Today’s Intelligent ApplicationsNeo4j
The document discusses how graph technology and Neo4j's graph data platform are fueling data-driven transformations across industries by unlocking deeper insights from relationships within data. It notes that 75% of Fortune 1000 companies had suppliers impacted by the pandemic showing supply chain problems are really data problems. It then promotes Neo4j as the leader in the growing graph database market and discusses its capabilities and customers across industries like insurance, banking, automotive, retail, and telecommunications.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
This document provides an overview of graph databases and their use cases. It begins with definitions of graphs and graph databases. It then gives examples of how graph databases can be used for social networking, network management, and other domains where data is interconnected. It provides Cypher examples for creating and querying graph patterns in a social networking and IT network management scenario. Finally, it discusses the graph database ecosystem and how graphs can be deployed for both online transaction processing and batch processing use cases.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
This document outlines an upcoming workshop on graph technology and data science using Neo4j. The workshop will cover knowledge graphs, graph algorithms, graph machine learning techniques, and use cases. It will include demonstrations of algorithms like node similarity and centrality measures on graphs. Attendees will learn how graph databases like Neo4j can power graph analytics and machine learning to gain insights from connected data.
This document provides an overview of an introduction to Neo4j workshop. The workshop covers what graphs are and why they are useful, identifying good graph scenarios, the anatomy of a property graph database and introduction to Cypher, and hands-on exercises using the movie graph on Neo4j Sandbox or AuraDB Free. It also previews using the Stackoverflow graph and discusses continuing one's graph learning journey through Neo4j's online training and resources.
The document provides an overview of the internal workings of Neo4j. It describes how the graph data is stored on disk as linked lists of fixed size records and how two levels of caching work - a low-level filesystem cache and a high-level object cache that stores node and relationship data in a structure optimized for traversals. It also explains how traversals are implemented using relationship expanders and evaluators to iteratively expand paths through the graph, and how Cypher builds on this but uses graph pattern matching rather than the full traversal system.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
Amsterdam - The Neo4j Graph Data Platform Today & TomorrowNeo4j
This document provides an overview of the Neo4j Graph Data Platform. Some key points:
- Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases.
- The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB.
- Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data
The document discusses using graph databases and analytics to map customer journeys. It provides an agenda for the presentation, including why customer journeys are important to analyze, how the customer journey forms a graph, a demo of Bonsai's graph-based customer analytics tools, how the tools were built using Neo4j and Keylines, and three real-life examples where the tools helped companies. The presentation encourages moving Bonsai's Prescriptive Insights tool from alpha to beta testing and outlines the benefits expected from further development.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Family tree of data – provenance and neo4jM. David Allen
The document discusses using Neo4j, a graph database, to store and query provenance data. Some key points:
- Storing provenance in a relational database requires complex SQL and pushes graph operations into code, hurting performance on graph queries.
- Neo4j uses the Cypher query language which allows declarative graph queries without imperative code.
- Example Cypher queries are provided to demonstrate retrieving paths and relationships in a provenance graph.
- While graph databases provide better performance for graph queries, they have limitations for certain bulk scans compared to relational databases. Proper graph design is important.
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML).
With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices.
That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:
- Different approaches to graph feature engineering, from queries and algorithms to embeddings
- How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks
- How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data
- Why no-code visualization and prototyping is important
In diesem Webinar wollen wir einen Überblick über unser Angebot für Data Scientsts geben und zeigen, was heute schon relativ einfach und schnell möglich ist.
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...Neo4j
The document discusses how knowledge graphs and graph data science can provide more context and enable better predictions. It provides examples of using knowledge graphs for interactive browsing of patent and pathway data, cross-species ontology graph queries, identifying relevant COVID-19 genes using graph algorithms, and sub-phenotyping patient populations using graph embeddings. The key message is that knowledge graphs harness relationships to provide deep, dynamic context for analytics and machine learning.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j
This document provides an overview of the Neo4j graph data platform and its capabilities for data science and analytics. It discusses Neo4j's native graph architecture, tools for data scientists and analysts, and how Neo4j enables graph data science across the machine learning lifecycle from feature engineering to model deployment. Algorithms, embeddings, and machine learning pipelines in Neo4j are highlighted. Integration with common data ecosystems is also covered.
The Data Platform for Today’s Intelligent ApplicationsNeo4j
The document discusses how graph technology and Neo4j's graph data platform are fueling data-driven transformations across industries by unlocking deeper insights from relationships within data. It notes that 75% of Fortune 1000 companies had suppliers impacted by the pandemic showing supply chain problems are really data problems. It then promotes Neo4j as the leader in the growing graph database market and discusses its capabilities and customers across industries like insurance, banking, automotive, retail, and telecommunications.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
This document provides an overview of graph databases and their use cases. It begins with definitions of graphs and graph databases. It then gives examples of how graph databases can be used for social networking, network management, and other domains where data is interconnected. It provides Cypher examples for creating and querying graph patterns in a social networking and IT network management scenario. Finally, it discusses the graph database ecosystem and how graphs can be deployed for both online transaction processing and batch processing use cases.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
This document outlines an upcoming workshop on graph technology and data science using Neo4j. The workshop will cover knowledge graphs, graph algorithms, graph machine learning techniques, and use cases. It will include demonstrations of algorithms like node similarity and centrality measures on graphs. Attendees will learn how graph databases like Neo4j can power graph analytics and machine learning to gain insights from connected data.
This document provides an overview of an introduction to Neo4j workshop. The workshop covers what graphs are and why they are useful, identifying good graph scenarios, the anatomy of a property graph database and introduction to Cypher, and hands-on exercises using the movie graph on Neo4j Sandbox or AuraDB Free. It also previews using the Stackoverflow graph and discusses continuing one's graph learning journey through Neo4j's online training and resources.
The document provides an overview of the internal workings of Neo4j. It describes how the graph data is stored on disk as linked lists of fixed size records and how two levels of caching work - a low-level filesystem cache and a high-level object cache that stores node and relationship data in a structure optimized for traversals. It also explains how traversals are implemented using relationship expanders and evaluators to iteratively expand paths through the graph, and how Cypher builds on this but uses graph pattern matching rather than the full traversal system.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
Amsterdam - The Neo4j Graph Data Platform Today & TomorrowNeo4j
This document provides an overview of the Neo4j Graph Data Platform. Some key points:
- Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases.
- The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB.
- Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data
The document discusses using graph databases and analytics to map customer journeys. It provides an agenda for the presentation, including why customer journeys are important to analyze, how the customer journey forms a graph, a demo of Bonsai's graph-based customer analytics tools, how the tools were built using Neo4j and Keylines, and three real-life examples where the tools helped companies. The presentation encourages moving Bonsai's Prescriptive Insights tool from alpha to beta testing and outlines the benefits expected from further development.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Family tree of data – provenance and neo4jM. David Allen
The document discusses using Neo4j, a graph database, to store and query provenance data. Some key points:
- Storing provenance in a relational database requires complex SQL and pushes graph operations into code, hurting performance on graph queries.
- Neo4j uses the Cypher query language which allows declarative graph queries without imperative code.
- Example Cypher queries are provided to demonstrate retrieving paths and relationships in a provenance graph.
- While graph databases provide better performance for graph queries, they have limitations for certain bulk scans compared to relational databases. Proper graph design is important.
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML).
With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices.
That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:
- Different approaches to graph feature engineering, from queries and algorithms to embeddings
- How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks
- How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data
- Why no-code visualization and prototyping is important
In diesem Webinar wollen wir einen Überblick über unser Angebot für Data Scientsts geben und zeigen, was heute schon relativ einfach und schnell möglich ist.
Are You Underestimating the Value Within Your Data? A conversation about grap...Neo4j
Are You Underestimating the Value Within Your Data?
A conversation about graph technology
Dr Jesús Barrasa
Head of Field Engineering, Neo4j
Dr Jim Webber
Chief Scientist, Neo4j
Gartner IT Symposium Xpo Barcelona 2022 - Neo4j
GraphSummit Toronto: Keynote - Innovating with Graphs Neo4j
Jim Webber Ph.D., Chief Scientist, Neo4j
Learn about the importance of graph technology, its evolution over the last few years and the impact it has had on the database and data analytics industry. This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases and graph data science can assist and accelerate machine learning and artificial intelligence projects.
This document discusses how graphs and graph databases can be used for data science and machine learning. It provides an overview of Neo4j's graph data science capabilities including graph algorithms, machine learning techniques, and real-world use cases.
The key points are:
1) Neo4j provides a graph data science library with over 70 graph algorithms and machine learning methods that can be used for tasks like link prediction, node classification, and graph feature engineering.
2) The library allows for both unsupervised and supervised machine learning on graph data in order to identify patterns, anomalies, and make predictions.
3) Real-world examples are presented where companies have used Neo4j's graph data
The document outlines an agenda for a workshop on building a graph solution using a digital twin data set. It includes sections on logistics, introductions, explaining the use case of a digital twin for a rail network, modeling the graph database solution, building the solution, and a question and answer period. Key aspects covered include an overview of Neo4j's graph database capabilities, modeling the domain entities and relationships, and exploring sample data related to operational points, sections, and points of interest for a rail network digital twin use case.
Using Connected Data and Graph Technology to Enhance Machine Learning and Art...Neo4j
This document discusses how using connected graph data and graph technologies can improve machine learning and artificial intelligence. It notes that relationships are highly predictive and underutilized in data, and that graphs provide a natural way to store and leverage relationship data. Graph databases allow incorporating these relationships into predictive models to produce more accurate results.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Graph Data Science: The Secret to Accelerating Innovation with AI/MLNeo4j
The document discusses how graph data science can accelerate AI and machine learning by leveraging relationships between data, which traditional approaches often ignore. It describes Neo4j's graph database and graph data science platform that allows users to perform queries, machine learning, and visualization on graph data to gain insights. Neo4j's graph data science library provides algorithms, embeddings, and in-graph machine learning models to make predictions that incorporate a graph's structural relationships.
GraphSummit Toronto: Leveraging Graphs for AI and MLNeo4j
This document discusses how graph data science can be used as a secret ingredient for relationship-driven AI. It explains that traditional machine learning ignores network structure, but graph databases can store and retrieve relationships to make AI more contextual. Graph algorithms and embeddings can infer relationships and enrich data. The document provides examples of how knowledge graphs can be used for applications like recommendations, fraud detection, and knowledge management. It also outlines the key components of graph data science including graph algorithms, machine learning workflows, and the Neo4j graph database platform.
El camino hacia el éxito con las bases de datos de grafos, la ciencia de dato...Neo4j
This document discusses using graph databases, graph data science, and generative AI to unlock insights from connected data. It highlights how relationships in data are valuable, and how graph databases provide an intuitive way to represent and query relationship data. The document introduces Neo4j's graph database capabilities, including graph algorithms for analytics, machine learning on graphs, and integration with other data systems. It also discusses using Neo4j to ground language models for more accurate generative AI applications.
Neo4j provides a graph database and tools for working with connected data. It helps customers gain insights from data relationships. Neo4j has thousands of customers worldwide, supports various deployment options, and its graph queries and algorithms help with tasks like recommendations, fraud detection, and knowledge discovery. It provides visualizations, analytics and machine learning tools to make graph data accessible and help users understand connections.
Graph Data Science with Neo4j: Nordics WebinarNeo4j
This document is a presentation on graph data science with Neo4j. It discusses how relationships are strong predictors of behavior but are often ignored in traditional data science techniques. Graphs allow relationships to be built in. It promotes using Neo4j's graph algorithms and machine learning capabilities to perform tasks like clustering, classification, and link prediction on graph data in order to gain insights. A live demo is offered and resources for learning more about graph data science are provided.
It was a cool experience, spending time with programmer and some computer engineers. In this codecamp, I talked about the science behind Complex networks, and how to program for complex network analysis. I also had a brief introduction towards graph databases.
Government GraphSummit: Optimizing the Supply ChainNeo4j
Michael Moore Ph.D., Principal, Partner Solutions and Neo4j Technology, Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Neo4j – The Fastest Path to Scalable Real-Time AnalyticsNeo4j
The document discusses how graph databases like Neo4j can enable real-time analytics at massive scale by leveraging relationships in data. It notes that data is growing exponentially but traditional databases can't efficiently analyze relationships. Neo4j natively stores and queries relationships to allow analytics 1000x faster. The document advocates that graphs will form the foundation of modern data and analytics by enhancing machine learning models and enabling outcomes like building intelligent applications faster, gaining deeper insights, and scaling limitlessly without compromising data.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
La strada verso il successo con i database a grafo, la Graph Data Science e l...Neo4j
This document discusses using generative AI and knowledge graphs. It begins by introducing Neo4j and graph databases. It then discusses how graph data science techniques can be applied to areas like predictive maintenance and fraud detection. Next, it covers generative AI and challenges like knowledge cut-off and bias. The document proposes grounding language models in knowledge graphs to improve accuracy, enable deployment with confidence, and unlock innovation. It suggests Neo4j is well-suited to ground language models due to its flexible schema, security, scalability and support for graph data science.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
This document provides an agenda and overview for a presentation on using graph algorithms in banking. The presentation introduces graphs and the Neo4j graph database, demonstrates sample banking data modeled as a graph, and reviews several graph algorithms that could be used for applications like fraud detection, including PageRank, weakly connected components, node similarity, and Louvain modularity. The document concludes with a demo and Q&A section.
Similar to Workshop - Neo4j Graph Data Science (20)
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Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
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Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
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Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
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What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
UI5con 2024 - Bring Your Own Design SystemPeter Muessig
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
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Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Drona Infotech is a premier mobile app development company in Noida, providing cutting-edge solutions for businesses.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
2. Neo4j, Inc. All rights reserved 2022
Neo4j is a Native Graph Database
2
3. Neo4j, Inc. All rights reserved 2022
Relational VS Graph models
3
Relational Model Graph Model
KNOWS
KNOWS
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person Friend
Person-Friend
ANDREAS
DELIA
TOBIAS
MICA
4. Neo4j, Inc. All rights reserved 2022
Labeled property graph model components
● Nodes
- Represent objects in the graph
● Relationships
- Relate nodes by type and direction
● Properties
- Name-value pairs that can go
on nodes and relationships
- Can have indexes and composite indexes
(types: String, Number, Long, Date, Spatial, byte
and arrays of those)
● Labels
- Group nodes
- Shape the domain
4
CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10,
2011
brand: “Volvo”
model: “V70”
LOVES
LIVES WITH
O
W
N
S
PERSON PERSON
LOVES