Graph

Adding Context Will Take AI to the Next Level | Neo4j

How Graphs are Changing AI Speaker: Amy Hodler, Neo4j Abstract: Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible and explainable. Join us to hear about the areas at the forefront of graph enhanced AI and ML, and find out which techniques are commonly used today and which hold the potential for disrupting industries. We'll look at the phases of graph enhanced AI as well as future-looking trends

Graph databases: The best kept secret for effective AI Emil Eifrem, Neo4j Co-Founder and CEO explains why connected data is the key to more accurate, efficient and credible learning systems. Using real world use cases ranging from space engineering to investigative journalism, he will outline how a relationships-first approach adds context to data - the key to explainable, well-informed predictions. Wish you were here? Sign up for 2 for 1 discount code for #WebSummit 2019 now: http://news.websummit.com/live-stream

How Graph Technology Is Changing Artificial Intelligence and Machine Learning Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible. Join us to hear about 6 areas at the forefront of graph enhanced AI and ML, and find out which techniques are commonly used today and which hold the potential for disrupting industries. Amy Hodler and Jake Graham, Neo4j #ArtificialIntelligence #GraphTechnology #GraphConnect

Graphs for AI and ML Presented by Jim Webber, Chief Scientist at Neo4j. Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible.

Graph Databases Will Change Your Freakin' Life (Best Intro Into Graph Databases) Ed Finkler http://nodevember.org/talk/Ed%20Finkler ## WTF is a graph database - Euler and Graph Theory - Math -- it's hard, let's skip it - It's about data -- lots of it - But let's zoom in and look at the basics ## Relational model vs graph model - How do we represent THINGS in DBs - Relational vs Graph - Nodes and Relationships ## Why use a graph over a relational DB or other NoSQL? - Very simple compared to RDBMS, and much more flexible - The real power is in relationship-focused data (most NoSQL dbs don't treat relationships as first-order) - As related-ness and amount of data increases, so does advantage of Graph DBs - Much closer to our whiteboard model - Answering questions you didn't expect ## Let's look at some examples * A bit o' live code * Based on OSMI mental health in tech survey graph ## How do we use this from a programming lang? * Neo4j 3.x uses a RESTful API and a native protocol (BOLT) * All client libraries are wrappers for this * Show a couple code examples with popular wrappers ## Resources * Graph Story OSMI Graph Blog * Neo4j docs # More Resources

An Introduction to Graph Neural Networks: Models and Applications MSR Cambridge, AI Residency Advanced Lecture Series An Introduction to Graph Neural Networks: Models and Applications Got it now: "Graph Neural Networks (GNN) are a general class of networks that work over graphs. By representing a problem as a graph — encoding the information of individual elements as nodes and their relationships as edges — GNNs learn to capture patterns within the graph. These networks have been successfully used in applications such as chemistry and program analysis. In this introductory talk, I will do a deep dive in the neural message-passing GNNs, and show how to create a simple GNN implementation. Finally, I will illustrate how GNNs have been used in applications. More info

GraphConnect SF 2015 / Karen Lopez, InfoAdvisors - 7 Ways Your Data Is Telling You It’s a Graph Karen Lopez, Sr. Project Manager, InfoAdvisors

Graph Representation Learning (Stanford University) Machine Learning TV Slide link

Yann LeCun - Graph Embedding, Content Understanding, and Self-Supervised Learning Institut des Hautes Études Scientifiques (IHÉS)

A Skeptics Guide to Graph Databases - David Bechberger Graph databases are one of the hottest trends in tech, but is it hype or can they actually solve real problems? Well, the answer is both. In this talk, Dave will pull back the covers and show you the good, the bad, and the ugly of solving real problems with graph databases. He will demonstrate how you can leverage the power of graph databases to solve difficult problems or existing problems differently. He will then discuss when to avoid them and just use your favorite RDBMS. We will then examine a few of his failures so that we can all learn from his mistakes. By the end of this talk, you will either be excited to use a graph database or run away screaming, either way, you will be armed with the information you need to cut through the hype and know when to use one and when to avoid them. Check out more of our talks in the following links! NDC Conferences http://ndcoslo.com http://ndcconferences.com

Graph Data Models

Graph Fundamentals | Kevin Feeney TerminusDB - Medium
RDF Triple Stores vs. Labeled Property Graphs: What’s the Difference? | Jesús Barrasa - Neo4j
- Labeled Property Graph (LPG)
- Resource Description Framework (RDF) Graph
- Others

Graph Use Cases

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Machine Learning
Portfolio Analytics (Asset Management)
Master Data Management
Data Integration
Social Networks
Genomics (Gene Sequencing) BioInformatics
Epidemiology
Web Browsing
Semantic Web
Communication Networks (Network Cell Analysis)
Internet of Things (Sensor Networks)
Recommendations
Fraud Detection (Money Laundering)
Geo Routing (Public Transport)
Customer 360
Insurance Risk Analysis
Content Management & Access Control
Privacy, Risk and Compliance

Graph query-programming languages

GraphQL

Youtube search... ...Google search

GraphQL
GraphiQL is the reference implementation of GraphQL IDE, an official project under the GraphQL Foundation
GitHub GraphQL API
Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning

GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. GraphQL queries access not just the properties of one resource but also smoothly follow references between them. While typical REST APIs require loading from multiple URLs, GraphQL APIs get all the data your app needs in a single request. Apps using GraphQL can be quick even on slow mobile network connections.

With Python

Cypher

Youtube search... ...Google search

openCypher.org - Originally contributed by Neo4j; transition planning from openCypher implementations to the developing graph query language standard, GQL

Graph Query Language (GQL)

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Property Graphs Explained | Thomas Frisendal

Will enable SQL users to use property graph style queries on top of SQL tables. GQL draws heavily on existing languages. The main inspirations have been Cypher (now with over ten implementations, including six commercial products), Oracle's PGQL and SQL itself, as well as new extensions for read-only property graph querying to SQL. SQL ... and now GQL | Alastair Green

Gremlin

Youtube search... ...Google search

Wikipedia
Intro to Graph Databases Using Tinkerpop, TitanDB, and Gremlin | Caleb Jones
Gremlin Graph Traversal Machine and Language
TinkerPop

is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation. Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors. As an explanatory analogy, Apache TinkerPop and Gremlin are to graph databases what the JDBC and SQL are to relational databases. Likewise, the Gremlin traversal machine is to graph computing as what the Java virtual machine is to general purpose computing.

Oracle Property Graph (PGQL)

Youtube search... ...Google search

Property Graph Query Language is a query language built on top of SQL, bringing graph pattern matching capabilities to existing SQL users as well as to new users who are interested in graph technology but who do not have an SQL background.

SPARQL

Youtube search... ...Google search

SPARQL a query language for RDF databases

Graph Databases (GDB)

...offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on).