Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
Government GraphSummit: Leveraging Graphs for AI and MLNeo4j
Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j
Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.
Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Les graphes de connaissances et le Machine Learning sont les deux principales méthodes de représentation et d’exploitation des connaissances. Ce qui est intéressant et plutôt méconnu, c’est qu’ils sont hautement complémentaires. Cette présentation vous éclairera sur la manière dont ces deux domaines interagissent, mettant en avant la synergie entre eux et comment les variantes les plus récentes du ML (IA générative et LLMs) s’intègrent avec des graphes de connaissances pour construire des applications sémantiques modernes.
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.
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
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
Government GraphSummit: Leveraging Graphs for AI and MLNeo4j
Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j
Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.
Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Les graphes de connaissances et le Machine Learning sont les deux principales méthodes de représentation et d’exploitation des connaissances. Ce qui est intéressant et plutôt méconnu, c’est qu’ils sont hautement complémentaires. Cette présentation vous éclairera sur la manière dont ces deux domaines interagissent, mettant en avant la synergie entre eux et comment les variantes les plus récentes du ML (IA générative et LLMs) s’intègrent avec des graphes de connaissances pour construire des applications sémantiques modernes.
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.
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
Neo4j : la voie du succès avec les bases de données de graphes et la Graph Da...Neo4j
GraphSummit Paris
Nicolas Rouyer, Senior Presales Consultant, Neo4j
L’innovation produit évolue rapidement chez Neo4j – découvrez comment la technologie des graphes peut vous fournir les outils nécessaires pour obtenir beaucoup plus de vos données.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
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.
The path to success with Graph Database and Graph Data ScienceNeo4j
What’s new and what’s next? Product innovation moves rapidly at Neo4j – learn how graph technology can provide you with the tools to get much more from your data!
Neo4j_Anurag Tandon_Product Vision and Roadmap.Benelux.pptx.pdfNeo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Graphs make implicit relationships explicit and graph data science infers new relationships, derives semantics, and enriches the overall context transforming the graphs with natural relationships to truly knowledge graphs. In this session, let’s talk about the journey from graphs to knowledge graphs and leveraging unsupervised graph algorithms and graph analytics to analyze the complex features in your data and deliver deeper insights.
Workshop 1. Architecting Innovative Graph Applications
Join this hands-on workshop for beginners led by Neo4j experts guiding you to systematically uncover contextual intelligence. Using a real-life dataset we will build step-by-step a graph solution; from building the graph data model to running queries and data visualization. The approach will be applicable across multiple use cases and industries.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
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
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...Neo4j
Roberto Sannino, Larus Business Automation
Nel panorama sempre più complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono l’ecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how è stato incanalato nell’innovativa soluzione Galileo.XAI di LARUS, un prodotto all’avanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dell’Intelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerà il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialità della soluzione Galileo.XAI nel guidare l’innovazione e la trasformazione digitale.
GraphSummit Milan - Visione e roadmap del prodotto Neo4jNeo4j
van Zoratti, VP of Product Management, Neo4j
Scoprite le ultime innovazioni di Neo4j che consentono un’intelligenza guidata dalle relazioni su scala. Scoprite le più recenti integrazioni nel cloud e i miglioramenti del prodotto che rendono Neo4j una scelta essenziale per gli sviluppatori che realizzano applicazioni con dati interconnessi e IA generativa.
GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with GraphNeo4j
Dr Jesús Barrasa, Head of Solutions Architecture for EMEA, 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.
LARUS - Galileo.XAI e Gen-AI: la nuova prospettiva di LARUS per il futuro del...Neo4j
Roberto Sannino, Product Owner, Larus Business Automation
Nel panorama sempre più complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono l’ecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how è stato incanalato nell’innovativa soluzione Galileo.XAI di LARUS, un prodotto all’avanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dell’Intelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerà il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialità della soluzione Galileo.XAI nel guidare l’innovazione e la trasformazione digitale.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
1. Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
1
Introducing to the Power of Graph
Technology
Kristof Neys,
Graph Data Science Specialist, Field Engineering EMEA/APAC
May 2022
3. Neo4j, Inc. All rights reserved 2021
3
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
4. Neo4j, Inc. All rights reserved 2021
User
:VISITED
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:VISITED
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:
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:VISITED
:SAME_AS
Graphs allows you to make implicit
relationships….
….explicit
Graphs….Grow!
5. Neo4j, Inc. All rights reserved 2021
:SAME_AS
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:VISITED
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…and can then group similar nodes…and
create a new graph from the explicit
relationships…
A graph grows organically - gaining
insights and enriching your data
Graphs….Grow!
6. Neo4j, Inc. All rights reserved 2021
Not that long ago…. Deepmind stated...
“We argue that combinatorial
generalisation must be a top
priority for AI to achieve
human-like abilities, and that
structured representations [i.e.
Graphs] and computations are
key to realizing this objective”
7. Neo4j, Inc. All rights reserved 2021
Everything is a Graph...
8. Neo4j, Inc. All rights reserved 2021
8
results from https://dimensions.ai, a
site that tracks research papers. The
search was for "graph neural
network" OR "graph convolutional"
OR "graph embedding" OR "graph
learning" OR "graph attention" OR
"graph kernel" OR "graph
completion"
Because I say so others say so!
9. Neo4j, Inc. All rights reserved 2021
Graph Neural Networks are HOT!
10. Neo4j, Inc. All rights reserved 2021
“By 2025, graph technologies will be
used in 80% of data and analytics
innovations...”
Top 10 Trends in Data and Analytics, 11 May 2020, Rita Sallam et al.
11. Neo4j, Inc. All rights reserved 2021
11
What can Neo4j Graph Data Science do?
12. Neo4j, Inc. All rights reserved 2021
Neo4j’s Graph Data Science Framework
Neo4j Graph Data
Science Library
Neo4j
Database
Neo4j
Bloom
Scalable Graph Algorithms &
Analytics Workspace
Native Graph Creation &
Persistence
Visual Graph
Exploration & Prototyping
13. Neo4j, Inc. All rights reserved 2021
13
Graphs & Data Science
Knowledge Graphs
Graph Algorithms
Graph Native
Machine Learning
Find the patterns you’re
looking for in connected data
Use unsupervised machine
learning techniques to
identify associations,
anomalies, and trends.
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
Train in-graph supervised ML
models to predict links,
labels, and missing data.
14. Neo4j, Inc. All rights reserved 2021
14
Before we go any further…let’s
quiz!
15. Neo4j, Inc. All rights reserved 2021
15
Which of the colored nodes would be considered the most
‘important'?
16. Neo4j, Inc. All rights reserved 2021
16
Which of the colored nodes would be considered the most
‘important'?
17. Neo4j, Inc. All rights reserved 2021
17
60+ Graph Data Science Techniques in Neo4j
Pathfinding &
Search
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• A* Shortest Path
• Yen’s K Shortest Path
• Minimum Weight Spanning Tree
• K-Spanning Tree (MST)
• Random Walk
• Breadth & Depth First Search
Centrality &
Importance
• Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Hyperlink Induced Topic Search (HITS)
• Influence Maximization (Greedy, CELF)
Community
Detection
• Triangle Count
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Speaker Listener Label Propagation
Supervised
Machine Learning
• Node Classification
• Link Prediction
… and more!
Heuristic Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
Similarity
• Node Similarity
• K-Nearest Neighbors (KNN)
• Jaccard Similarity
• Cosine Similarity
• Pearson Similarity
• Euclidean Distance
• Approximate Nearest Neighbors (ANN)
Graph
Embeddings
• Node2Vec
• FastRP
• FastRPExtended
• GraphSAGE
• Synthetic Graph Generation
• Scale Properties
• Collapse Paths
• One Hot Encoding
• Split Relationships
• Graph Export
• Pregel API (write your own algos)
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How can they be used?
Stand Alone Solution
Find significant patterns and optimal
structures
Use community detection and
similarity scores for recommendations
Machine Learning Pipeline
Use the measures as features to train
an ML model
1st
node
2nd
node
Common
neighbors
Preferential
attachment
Label
1 2 4 15 1
3 4 7 12 1
5 6 1 1 0
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Our Implementations are Fast - and Getting Faster
LDBC100
(LDBC Social Network Scale Factor 100)
300M+ nodes
2B+ relationships
LDBC100PKP
(LDBC Social Network Scale Factor 100)
500k nodes
46M+ relationships
Logical Cores: 64
Memory: 512GB
Storage: 600GB
NVMe-SSD
AWS EC2 R5D16XLarge
Intel Xeon Platinum 8000
(Skylake-SP or Cascade Lake)
Node Similarity
20min
Betweenness Centrality
10min
Node2Vec
2.8min
Label Propagation
46sec
Weakly Connected
Components
36sec
Local Clustering
Coefficient
4.76min
FastRP
1.33min
PageRank
53sec
Louvain
14.66min
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It’s all about Embeddings…
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Node Embedding
What are node embeddings?
How?
The representation of nodes as low-dimensional vectors that
summarize their graph position, the structure of their local graph
neighborhood as well as any possible node features
Encoder - Decoder Framework
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Graph Embeddings in Neo4j
Node2Vec
Random walk based embedding
that can encode structural similarity
or topological proximity.
Easy to understand, interpretable
parameters, plenty of examples
GraphSAGE
Inductive embedding that encodes
properties of neighboring nodes when
learning topology.
Generalizes to unseen graphs, first
method to incorporate properties
FastRP
A super fast linear algebra based
approach to embeddings that can
encode topology or properties.
75,000x faster than Node2Vec
extended to encode properties
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Graph Machine Learning
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Node Classification - in Neo4j
Load your in- memory
graph with labels &
features
Use
nodeClassification.train
Specify the property you want to
predict and the features for making
that prediction
Node classification:
Predicting a node label or (categorical) property
Neo4j Automates the Tricky Parts:
1. Splits data for train & test
2. Builds logistic regression models using the training data
& specified parameters to predict the correct label
3. Evaluates the accuracy of the models using the test data
4. Returns the best performing model
The predictive model
appears in the model
catalog, ready
to apply to
new data
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Link Prediction - in Neo4j
Load your in- memory
graph with labels &
features
Use
linkPrediction.train
Split your graph into train & test
splitRelationships.mutate
Link Prediction:
Predicting unobserved edges or relationships that will form in the future
Neo4j Automates the Tricky Parts:
1. Builds logistic regression models using the training data
& specified parameters to predict the correct label
2. Evaluates the accuracy of the models using the test data
3. Returns the best performing model
The predictive model
appears in the model
catalog, ready
to apply to
new data
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Neo4j working with AWS
Sagemaker
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Neo4j in the AWS Ecosystem
AWS Cloud
Kafka Connect
Plugin
Connector for Apache
Spark
Neo4j Graph Data
Science
Neo4j Graph
Database
Neo4j
Bloom
Database Business Intelligence
Analytics
Connector for BI
Amazon S3
Amazon SageMaker
Amazon Managed
Streaming for Apache Kafka
Amazon QuickSight
Amazon EMR
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Neo4j and SageMaker
1. Generate graph feature embeddings in Neo4j
Graph Data Science
2. Export to S3
3. Import into SageMaker
4. Supervised Learning
AWS Cloud
Amazon SageMaker
Amazon S3
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Banking Fraud: A use case
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Accelerated Fraud Detection in FinTech
• As part of a larger business transformation initiative, they wanted to
reduce cost and time associated with money transfers
• False negatives in AML/Fraud create delays during investigation and
ultimately unhappy customers
• From established base in EU, rapid expansion into US and APAC meant
compliance to new regulations and laws
• Current home-grown solution was too slow (manual), expensive and didn’t
scale as fast as the business growth
• Traditional rules-based approach only focused on known issues while
fraudsters “think ahead”
The Challenge:
“We had been an Insights customer for years with Synthetics but needed to better
understand the real experience of our customers and how they were impacted by
changes to the site. Business Analytics with Insights solved that for us.”
● International B2B payment delivery and banking services provider
Founded in 2013, headquartered in Luxembourg with 200 customers
● Processing 6% of European B2C e-commerce payments in 2020 and
over 250 billion Euros in payments volumes
● Delivers rapid access to direct clearing and partner banks enabling
cross-border payments in 25 currencies
• Required an on-going, scalable and supported
solution as opposed to “throwing bodies at the
problem”
• Sought a forward-looking, Machine Learning
(ML) solution compatible with their AWS
architecture
• Insights customer for 10 years
• Dynatrace DEM (RUM + Synthetic) for 2 years
The Requirements:
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Reduced false negatives & alerts = ROI in months
“We had been an Insights customer for years with Synthetics but needed to better
understand the real experience of our customers and how they were impacted by
changes to the site. Business Analytics with Insights solved that for us.”
“For AML, when you visualize all the connections on a
screen, you can very easily spot important items: what used
to take 3+ days to look for a connection can be found in
less than 30 seconds.”
Ruben Menke - Sr Data Scientist, Banking Circle
• Reduced false negatives by 25%
• Decreased numbers of overall alerts escalated for manual reviews by 50%
• Empowered non-technical users (i.e. investigators) to gain instant insights
in graph form
• ML approach optimizes information from customers to see patterns and
build models based on real-time data
The Solution:
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Neo4j and AWS
● >40% of Neo4j customers run on AWS
● Member of Amazon Partner Network since 2013
● APN Advanced Tier Partner
○ AWS ISV Workload Migration
○ APN Global Startup
○ ISV Accelerate
● Collaborative Joint Engineering
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On the AWS Marketplace:
● Neo4j Enterprise
○ AMI and CFT
○ BYOL
○ Graph Database
○ Graph Data Science
○ Bloom
● AuraDB Enterprise
○ DBaaS
Getting Started
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Thank you!
● Try it yourself!
○ Neo4j-Partners GitHub
○ AWS Quick Start
● Further Reading
○ Landing Page
○ APN Partner Finder
● Contact us: ecosystem@neo4j.com
Q&A