Your Roadmap for An Enterprise Graph StrategyNeo4j
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
Your Roadmap for An Enterprise Graph StrategyNeo4j
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
Accelerate Innovation and Digital Transformation – How Neo4j Can HelpNeo4j
What’s the best way to understand your business challenge and accelerate innovation – Innovation Lab or Bootcamp? We would like to argue – it depends!
The Innovation Lab is an onsite Design Sprint, where we educate business and technical users on the potential of graph technology and explore use cases by prototyping graph projects together with our customers. They gain a deep understanding of Graph Thinking and the possibilities in innovation and digital transformation within their organization.
The Bootcamp is a more technical exercise where we educate the development team and co-create a small Proof of Concept based on a real-life dataset for a clearly identified use case.
So, it depends on the customer’s specific needs and stage from what they benefit most – designing and prototyping versus creating.
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...Neo4j
In this talk we'll explore powerful analytic techniques for graph data. Firstly we'll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we'll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we'll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.
Then we'll see how graph matching can be used to extract online business intelligence (for powerful retail recommendations). In turn we'll apply these powerful techniques to modelling domains in Neo4j (a graph database) and show how Neo4j can be used to drive business intelligence.
Don't worry, there won't be much maths :-)
Accelerate Innovation and Digital Transformation – How Neo4j Can HelpNeo4j
What’s the best way to understand your business challenge and accelerate innovation – Innovation Lab or Bootcamp? We would like to argue – it depends!
The Innovation Lab is an onsite Design Sprint, where we educate business and technical users on the potential of graph technology and explore use cases by prototyping graph projects together with our customers. They gain a deep understanding of Graph Thinking and the possibilities in innovation and digital transformation within their organization.
The Bootcamp is a more technical exercise where we educate the development team and co-create a small Proof of Concept based on a real-life dataset for a clearly identified use case.
So, it depends on the customer’s specific needs and stage from what they benefit most – designing and prototyping versus creating.
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...Neo4j
In this talk we'll explore powerful analytic techniques for graph data. Firstly we'll discover some of the innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we'll be able to predict how the graph will evolve over time. To test just how powerful and accurate graph theory is, we'll also be able to (retrospectively) predict World War 1 based on a social graph and a few simple mechanical rules.
Then we'll see how graph matching can be used to extract online business intelligence (for powerful retail recommendations). In turn we'll apply these powerful techniques to modelling domains in Neo4j (a graph database) and show how Neo4j can be used to drive business intelligence.
Don't worry, there won't be much maths :-)
Introduction to graph databases in term of neo4jAbdullah Hamidi
The records in a graph database are called Nodes .
Nodes are connected through typed, directed Relationships.
Each single Node and Relationship can have named attributes referred to as Properties.
A Label is a name that organizes nodes into groups.
The flexibility of the graph model has allowed us to add new nodes and new relationships.
Relationships in a graph naturally form paths. Querying—or traversing—the graph involves following paths.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...Daniel Zivkovic
Serverless Toronto's 6th-anniversary event helps IT pros understand and prepare for the #GenAI tsunami ahead. You'll gain situational awareness of the LLM Landscape, receive condensed insights, and actionable advice about RAG in 2024 from Google AI Lead Mark Ryan and LlamaIndex creator Jerry Liu. We chose #RAG (Retrieval-Augmented Generation) because it is the predominant paradigm for building #LLM (Large Language Model) applications in enterprises today - and that's where the jobs will be shifting. Here is the recording: https://youtu.be/P5xd1ZjD-Os?si=iq8xibj5pJsJ62oW
Graph Databases - Where Do We Do the Modeling Part?DATAVERSITY
Graph processing and graph databases have been with us for a while. However, since their physical implementations are the same for every database in production (Node connected to node, or triplets), there's a perception that data modeling (and data modelers) have no role on projects where graph databases are used.
This month we'll talk about where graph databases are a best fit in a modern data architecture and where data models add value.
Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful.
Our neural networks can take questions and knowledge graphs and return answers. Imagine:
a google assistant that reads your own knowledge graph (and actually works)
a BI tool reads your business' knowledge graph
a legal assistant that reads the graph of your case
Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages.
Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases.
Improve ml predictions using graph algorithms (webinar july 23_19).pptxNeo4j
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this webinar, we’ll focus on using graph feature engineering to improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll illustrate a link prediction workflow using Spark and Neo4j to predict collaboration and discuss our missteps and tips to get to measurable improvements.
The future is here and the future are Graph Databases! Have a lot of interconnected data dat you want to extract value and meaning from it? Having too many joins that are running too slow? Do you want to do Real-Time Recommendations? Read this!
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
The trend nowadays is to represent the relationships between entities in a graph structure. Neo4j is a NOSQL graph database, which allows for fast and effective queries on connected data. Implementation of own algorithms is possible, which can improve the functionality of built in API. We make use of the graph database to model and recommend movies and other media content.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
Improving Machine Learning using Graph AlgorithmsNeo4j
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this session, we’ll focus on how using connected features can help improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll look at a link prediction example to predict collaboration with measurable improvement when including graph-based features.
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.
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.
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.
As a business owner in Delaware, staying on top of your tax obligations is paramount, especially with the annual deadline for Delaware Franchise Tax looming on March 1. One such obligation is the annual Delaware Franchise Tax, which serves as a crucial requirement for maintaining your company’s legal standing within the state. While the prospect of handling tax matters may seem daunting, rest assured that the process can be straightforward with the right guidance. In this comprehensive guide, we’ll walk you through the steps of filing your Delaware Franchise Tax and provide insights to help you navigate the process effectively.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
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Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
Attending a job Interview for B1 and B2 Englsih learnersErika906060
It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
Memorandum Of Association Constitution of Company.ppt
GraphTour Boston - Graphs for AI and ML
1. Graphs for AI and ML
Dr. Jim Webber
Chief Scientist, Neo4j
@jimwebber
2. ● Some no-BS definitions
● Graphs and an accidental Skynet
● Graph theory
● Contemporary graph ML
● The future of graph AI
Overview
3. ● ML - Machine Learning
○ Finding functions from historical data to guide future
interactions within a given domain
● AI - Artificial Intelligence
● The property of a system that it appears intelligent to its users
● Often, but not always, using ML techniques
● Or ML implementations that can be cheaply retrained to address
neighboring domains
A Bluffer’s Guide to AI-cronyms
4. ● Predictive analytics
● Use past data to predict the future
● General purpose AI
● ML with transfer learning such that learned experiences in one
domain can be applied elsewhere
● Human-like AI
Often conflated with
7. Extract all the features!
• What do we do? Turn it to
vectors and pump it through a
classification or regression
model
• That’s actually not a bad
thing
• But we can do so much before
we even get to ML…
• … if we have graph data
10. • Nodes with optional properties and optional labels
• Named, directed relationships with optional properties
• Relationships have exactly one start and end node
• Which may be the same node
Labeled Property graph model
12. stole
from
loves
loves
enemy
enemy
A Good
Man Goes
to War
appeared
in
appeared
in
appeared
in
appeared
in
Victory of
the Daleks
appeared
in
appeared
in
companion
companion
enemy
planet
prop
species
species
species
character
character
character
episode
episode
23. Toolkit matures into
proper database
• Cypher and Neo4j server make
real time graph analytical
patterns simple to apply
• Amazing and humane to
implement
42. Graph Theory
• Rich knowledge of how graphs
operate in many domains
• Off the shelf algorithms to
process those graphs for
information, insight, predictions
• Low barrier to entry
• Amazingly powerful
61. It if a node has strong relationships to two neighbours, then these
neighbours must have at least a weak relationship between them.
[Wikipedia]
Strong Triadic Closure
64. • Relationships can have “strength” as well as intent
• Think: weighting on a relationship in a property graph
• Weak links play another super-important structural role in graph
theory
• They bridge neighbourhoods
Weak relationships
66. “If a node A in a network satisfies the Strong Triadic Closure Property
and is involved in at least two strong relationships, then any local
bridge it is involved in must be a weak relationship.”
[Easley and Kleinberg]
Local Bridge Property
68. • (NP) Hard problem
• Repeatedly remove the spanning links between dense regions
• Or recursively merge nodes into ever larger “subgraph” nodes
• Choose your algorithm carefully – some are better than others for
a given domain
• Can use to (almost exactly) predict the
break up of the karate club!
Graph Partitioning
75. Find and stop spammers
Extract graph structure over time
Not message content!
(Fakhraei et al, KDD 2015)
Learning to stop bad guys
Result: find and classify 70% spammers with 90% accuracy
76. Much of modern graph ML is still about turning graphs to vectors
Graph2Vec and friends
Highly complementary techniques
Mixing structural data and features gives better results
Better data into the model, better results out
But we don’t have to always vectorize graphs...
Graph ML
77. Knowledge Graphs
• Semantic domain knowledge for
inference and understanding
• E.g. eBay Google Assistant
• What’s the next best question to ask
when a potential customer says they
want a bag?
• Price? Function? Colour?
• Depends on context! Demographic,
history, user journey.
• Richly connected data makes the
system seem intelligent
• But it’s “just” data and algorithms in
reality
78. Graph Convolutional
Neural Networks
A general architecture for
predicting node and relationship
attributes in graphs.
(Kipf and Welling, ICLR 2017)
Credit: Andrew Docherty (CSIRO), YowData 2017
https://www.youtube.com/watch?v=Gmxz41L70Fg
79. Graph Networks for
Structured Causal Models
• Position paper from Google,
MIT, Edinburgh
• Structured representations and
computations (graphs) are key
• Goal: generalize beyond direct
experience
• Like human infants can
https://arxiv.org/pdf/1806.01261.pdf
ML - this is what nerds do. Sometimes ML is so compelling that it seems intelligent, but in reality it’s data and algorithms all the way down.
AI - train a system to classify animals, might also work on shoes. See: hot dog; not hot dog!
GP-AI - systems like AlphaGo might be an architecture to support this in future, but we’re not there today
GP-AI - systems like AlphaGo might be an architecture to support this in future, but we’re not there today
Here’s where we are mostly today. Row-oriented data.
Maybe some documents, maybe some columns, but mostly rows of data from arcane data models.
You already know graphs
People talk about Codd’s relational model being mature because it was proposed in 1969 – 49 years old.
Euler’s graph theory was proposed in 1736 – 282 years old.
Now we use the labelled property graph model. A very simple set of idioms that can build very sophisticated models.
Graphs are the most natural way to model most domains. You already know this because you draw graphs on a whiteboard, but you’ve never had the opportunity to take that down into the database before.
Nodes are a bit like documents, but they’re flat at present in Neo4j.
You pour data into your nodes and then connect them – easy peasy.
This enables high fidelity domain modeling because this is how your domains work.
And you don’t have to do this stuff in your application code – it’s right there in the database
Let’s prove it by exploring a fun domain…
Graphs are the most natural way to model most domains. You already know this because you draw graphs on a whiteboard, but you’ve never had the opportunity to take that down into the database before.
Nodes are a bit like documents, but they’re flat at present in Neo4j.
You pour data into your nodes and then connect them – easy peasy.
This enables high fidelity domain modeling because this is how your domains work.
And you don’t have to do this stuff in your application code – it’s right there in the database
Let’s prove it by exploring a fun domain…
If you want to know who followed Matt Smith, easy!
Traversing the regenerated (or any) relationship takes about 1/40 millionth of a second on this mac in a steady state database
What if you want to know who preceded Matt Smith?
Easy. Traverse the regenerated rels in the other way.
Cost? About 1/40 millionth of a second on this laptop in a steady state database.
Find all the paths to any doctor
OR
Just ask the database to find the shortest
Note this is a pretty loosely specified query: production queries name relationships and labels (and other predicates) to help narrow matches and lower latency.
But since we can traverse 40 million rels/sec, don’t be worried about those “joins”
My shortest path to Doctor Who?
All the way back to Autumn 2008
November 2007 met Emil at Øredev in Malmö Sweden
Java and Maven build-your-own-DBMS toolkit called Neo4j
Java Core API only
Long afternoon of loading data and writing a recommendation query...
Find the current customer
Find things they own
Find things that depend on the things they own
Sell
Repeat
All we did at first was understand the dependencies between products and bundles.
We never tried to upsell something incompatible. Never tried to sell them something they already owned. Never undersold them.
And it opened a world of possibilities to combine other graphs: demographic, social, geographical, municipal, network...
The system made intelligent suggestions, but it was not ML or AI, just graph queries. It was good.
Unexpectedly Powerful
Solved a problem in a long afternoon was meant to take years with OTS software
Applied same pattern to PoS retail recommendations, fraud detection… in subsequent months
Still amazed!
Effect: join Neo4j as Chief Scientist in 2010.
So let’s get into graphs.
Realtime retail recommendations.
Historical anecdote about beer and nappies.
We had a data model
Some of it taxonomical
Some of it stock-centric.
Some transactional
The insight here is that we have a typical young father who buys beer, nappies and a game console simply by reducing subgraph
We have a pattern to search for
We knew it was young fathers, but I bet your model would classify them as lazy, drunken, gamers right?
Now we look for young fathers – implied by beer and nappies purchases – who haven’t bought a game console.
Turn it to text. And…
Neo4j 2.0:
MATCH (u:User), (n:ProductType), (b:ProductType), (x:ProductType)
WHERE
n.name = "nappies" AND
b.name = "Beer" AND
x.name = "Xbox" AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(n) AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(b) AND
NOT((u)-[:BOUGHT]->()<-[:MEMBER_OF]-(x))
RETURN u
Neo4j 2.0:
MATCH (u:User), (n:ProductType), (b:ProductType), (x:ProductType)
WHERE
n.name = "nappies" AND
b.name = "Beer" AND
x.name = "Xbox" AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(n) AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(b) AND
NOT((u)-[:BOUGHT]->()<-[:MEMBER_OF]-(x))
RETURN u
Neo4j 2.0:
MATCH (u:User), (n:ProductType), (b:ProductType), (x:ProductType)
WHERE
n.name = "nappies" AND
b.name = "Beer" AND
x.name = "Xbox" AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(n) AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(b) AND
NOT((u)-[:BOUGHT]->()<-[:MEMBER_OF]-(x))
RETURN u
Neo4j 2.0:
MATCH (u:User), (n:ProductType), (b:ProductType), (x:ProductType)
WHERE
n.name = "nappies" AND
b.name = "Beer" AND
x.name = "Xbox" AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(n) AND
(u)-[:BOUGHT]->()<-[:MEMBER_OF]-(b) AND
NOT((u)-[:BOUGHT]->()<-[:MEMBER_OF]-(x))
RETURN u
This is fast: query latency is proportional to the amount of graph searched
Now called “network science”
First we need to talk about some local properties
A triadic closure is a local property of (social) graphs whereby if two nodes are connected via a path involving a third node, there is an increased likelihood that the two nodes will become directly connected in future.
This is a familiar enough situation for us in a social setting whereby if we happen to be friends with two people, ultimately there's an increased chance that those people will become direct friends too, since by being our friend in the first place, it's an indication of social similarity and suitability.
It’s called triadic closure, because we try to close the triangle.
We see this all the time – it’s likely that if we have two friends, that they will also become at least acquaintances and potentially friends themselves!
In general, if a node A has relationships to B & C then the relationship between B&C is likely to form – especially if the existing relationships are both strong.
This is an incredibly strong assertion and will not be typically upheld by all subgraphs in a graph. Nonetheless it is sufficiently commonplace (particularly in social networks) to be trusted as a predictive aid.
Sentiment plays a role in how closures form too – there is a notion of balance.
From a triadic closure perspective this is OK, but intuitively it seems odd.
Cartman’s friends shouldn’t be friends with his enemies. Nor should Cartman’s enemies be friends with his friends.
This makes sense – Cartman’s friend Craig is also an enemy of Cartman’s enemy Tweek
Two negative sentiments and one positive sentiment is a balanced structure – and it makes sense too since we gang up with our friends on our poor beleaguered enemy
Is this true?
Yes.
Is it nice?
No.
Is it realistic?
Oh yes.
Another balanced – and more pleasant – arrangement is for three positive sentiments, in this case mutual friends.
A starting point for a network of friends and enemies 100 years on from the armistice
Red links indicate enemy of relationship
Black links indicate friend of relationship
The Three Emperor’s league
Italy forms the with Austria and Germany – a balanced +++ triadic closure
If Italy had made only a single alliance (or enemy) it would have been unstable and another relationship would be likely to form anyway!
Triple Alliance
Russia becomes hostile to Austria and Germany – a balance --+ d triadic closure
becomes agnostic towards France.
German-Russian Lapse
The French and Russians ally, forming a balanced --+ triadic closure with the UK
French-Russian Alliance
The UK and France enter into the famous
Entente Cordiale
This produces an unbalanced ++- triadic closure with Russia, and the graph doesn’t like it.
The British and Russians form an alliance, thereby changing their previously unbalanced triadic closure into a balanced one.
Other local pressures on the graph make other closures form.
Italy becomes hostile to Russia, forming a balanced --+ closure with the France, and another balanced --+ closure with the UK.
Germany and the UK become hostile forming a balanced --+ closure with Austria and another balanced --+ closure with Italy
British-Russian Alliance
That WWI can be predicted without domain knowledge by iterating a graph and applying local structural constraints is nothing short of astonishing to me.
Note how the network slides into a balanced labeling — and into World War I.
A very surprising result: graphs don’t know about human conflicts.
In this case the string triadic closure property still holds – though it is a weak link that characterises the relationship between Stan and Cartman.
Given a starting graph, we can apply this simple local principal to see how it would evolve.
In this case the string triadic closure property still holds – though it is a weak link that characterises the relationship between Stan and Cartman.
Given a starting graph, we can apply this simple local principal to see how it would evolve.
A local bridge acts as a link – perhaps the only realistic link - between two otherwise distant (or separate) subgraphs.
Local bridges are semantically rich – they provide conduits for information flow between otherwise independent groups.
In this case DATING is a local bridge – it must also be a weak relationship according to our definition of a local bridge
Intuitively this makes sense – your girl/boyfriend is rather less important at age 8 than your regular friends, IIRC.
How do we identify local bridges?
Any weak link which would cause a component of the graph to become disconnected.
Being able to identify local bridges is important – in this case it’s the only know conduit to allow the girls and boys to communicate.
In real life local bridges are apparent in your organisation as experts (or managers); appear as nexus in fraud cases;
Zachary in the Journal of Anthropological Research 1977
Intuitively we can see “clumps” in this graph.
But how do we separate them out? It’s called minimum cut.
What’s interesting is that it’s mechanical – no domain knowledge is necessary.
There’s only one failure with the method Zachary chose to partition the graph: node 9 should have gone to the instructor’s club but instead went with the original president of the club (node 34).
Why? Because the student was three weeks away from completing a four-year quest to obtain a black belt, which he could only do with the instructor (node 1)
Other minimum cut approaches might deliver slightly different results, but on the whole it’s amazing you get such insight from an algorithm!
But is there enough information in the graph itself to predict the schism?
But is there enough information in the graph itself to predict the schism?
Actually neo4j already has a bunch of these algorithms.
Call them easily from Cypher
Emergent intelligence from the graph!
Efficiency for graph operations is paramount.
You don’t need huge macho clusters to do this.
Large payment provider, transaction history
A 300M node, ~18B rel graph pageranked with 20 iterations in less than 2 hours using the graph algos.
On commodity hardware.
Contemporary AI
Graph structure itself is rich.
In this example we don’t need to know the content of the messages to know they’re spam at high confidence, just their position in the graph.
Mine a vector of graph features, feed it into the trained model.
Graphs have a key advantage: structural context. Where is the node in the graph? Who are its neighbours? Etc.
That richness feeds into the model and makes it better, more accurate, more dependable.
PageRank, Degree, Neighbourhood, Colour, etc are all features that improve your ML outcomes but are only available from graphs.
ICLR = International Conference on Learning Representations
Graph of movies that a user liked.
Feed into neural net
Graph of users who rated one of those movies.
Feed into neural net.
Recurse through the data until you get to all the movies and all the users which are just embedding vectors (fancy hashes that place like near like in a vector space).
[Can change these vectors for features to avoid cold-starts, without changing overall architecture.]
Graph of back-propagated trained neural nets.
Incremental: Scalable for both training and prediction.
Extensible: bring in other graph layers!
Better than collaborative filtering because it can work on any graph, not just bipartite user-likes-movies graphs. E.g. User likes actor in movies with genre – much richer!
A bipartite graph, also called a bigraph, is a set of graph vertices decomposed into two disjoint sets such that no two graph vertices within the same set are adjacent. I.e. Users don’t connect to users, only to movies.
This is already happening - it’s YouTube’s recommender algorithm.
A growing realisation from leaders in the AI community: graph networks as the foundational building block for human-like AI.
Argue: combinatorial generalization must be a top priority for AI to achieve human-like abilities. Must be able to compose a finite set of elements in infinite ways (eg like language)
We draw analogies by aligning the relational structure between two domains and drawing inferences about one based on corresponding knowledge about the other (Gentner and Markman, 1997; Hummel and Holyoak, 2003). Hierarchies are critical.
Inductive bias: how the algorithm prioritises solutions.
Relational inductive biases to guide deep learning about entities, relations, and rules for composing them. I.e. the learning understands graphs
All this might seem hard at first – we’re used to tables, and our toolkits expect them.
Graphs changes this for the better. Once you get graphs, all the other things seem hard
“a vast gap between human and machine intelligence remains, especially with respect to efficient, generalizable learning”
70% of graph ML today is still turning graphs to vectors
E.g. deep walk - random walk through graph, assign vector node when encountered based on neighborhood
30% is truly graph AI - “differential neural computer” -> discern patterns that users can’t; write sophisticated algorithms (fraud, shortest path, etc) from incentive declarations.
E.g. no longer need a human expert to discover the “young father” pattern in our data, the machine learns it’s a valuable query in some contexts.
So enjoy using graphs for AI, but please remember graphs for good!