Graph

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• Context ... the next AI frontier
• Enterprise Architecture (EA)
• Explainable / Interpretable AI
• Computer Networks
• Network Pattern
• Law
• Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
• Connected Papers using Semantic Scholar to explore connected papers in a visual graph
• A Beginner's Guide to Graph Analytics and Deep Learning | Chris Nicholson - A.I. Wiki pathmind
• 7 Ways Your Data Is Telling You It’s a Graph | Karen Lopez - InfoAdvisors - Neo4j
• Linked Data Patterns book | leigh Dodds and Ian Davis
• A Guide To Knowledge Graphs | Mohit Mayank - TOPBOTS
• Generative AI
  • Large Language Model = Knowledge Graph Store? Yes, by fine-tuning LLM with KG | Peter Lawrence - Medium
  • ChatGPT (LLMs) vs. Knowledge Graphs | Kurt Cagle - The Cagle Report
  • Using Knowledge Graphs to Reduce Output Errors in Large Language Models | Doug Dunn

Adding Context Will Take AI to the Next Level | Neo4j

The Cagle Report. Issue #4. Interview With ChatGPT In which editor Kurt Cagle interviews ChatGPT about large language models and knowledge graphs. Posted on April 2, 2023 by Kurt Cagle

GraphGPT: Transform Text into Knowledge Graphs with GPT-3 GraphGPT converts unstructured natural language into a knowledge graph. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or a transcript from a video to generate a graph visualization of entities and their relationships. A classic use case of prompt engineering. Github: https://github.com/varunshenoy/GraphGPT Demo: https://graphgpt.vercel.app/ Enjoy reading articles? then consider subscribing to Medium membership, it just 5$ a month for unlimited access to all free/paid content. Subscribe now - https://prakhar-mishra.medium.com/membership

Knowledge Graphs and Deep Learning 102 In this video, we are going to look into not so exciting developments that connect Deep Learning with Knowledge Graph and GANs… let’s just hope it’s more fun than “Machine Learning Memes for Convolutional Teens”. GAN Explained Link The bot in the video is R2D2, which comes after OB1's 2nd gen in Star Wars. Audio change was a bit tricky. Topics Covered in the video 1. Graph Convolutional Networks 2. Semi-supervised Learning 3. Knowledge Graphs and Ontology 4. Embedding in Knowledge Graphs 5. Adversarial Learning in Knowledge Graphs (KBGANs) Please contribute to the initiative by donating to us via Patreon because we need the money to scale up our efforts and bring creative weirdos and nerdy dreamers together. Patreon Link: https://www.patreon.com/crazymuse Even something as small as 1$ per creation can make a collective difference. Join us on slack if you want to contribute to the scripts that we write for the video. Slack Link : https://goo.gl/GFW2My Contributors for the Video 1. Script Writer : Jaley Dholakiya 2. Reviewers : Arjun Shetty, Sidharth Aiyar, Saikat Paul 3. Animator and Moderator : Jaley Dholakiya Trans-D embedding : https://www.aclweb.org/anthology/P15-1067 KBGANs : https://arxiv.org/pdf/1711.04071.pdf

Knowledge Graphs & Deep Learning at YouTube Aurelien explains how you can combine Knowledge Graphs and Deep Learning to dramatically improve Search & Discovery systems. By using a combination of signals (audiovisual content, title & description and context), it is possible to find the main topics of a video. These topics can then be used to improve recommendations, search, structured browsing, ads, and much more. EVENT: dotAI 2018 SPEAKER: Aurelien Geron PERMISSIONS: dotconference Organizer provider Coding Tech with the permission to republish this video.

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: https://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 https://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 https://ndcoslo.com https://ndcconferences.com

The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry, and Physics James R. Lee, University of Washington Simons Institute Open Lectures Spectral geometry has long been a powerful tool in many areas of mathematics and physics. In a similar way, spectral graph theory has played an important role in algorithms. But the last decade has seen a revolution of sorts: Fueled by fundamental computational questions, spectral methods have been challenged to address new kinds of problems, and have proved their worth by providing a remarkable set of new ideas and solutions. Starting with a basic physical process (heat diffusion), we will recall how the evolution of the system can be understood in terms of eigenmodes and their associated eigenvalues. The physical view has immediate computational import; for instance, Google models its users as agents diffusing over the web graph. This family of ideas describes the "reasonable" effectiveness of spectral graph theory. But then we will see that spectral objects can also precisely describe other phenomena in a surprisingly unreasonable way. This will lead us to confront some of the most fundamental problems in algorithms and complexity theory from a spectral perspective.

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

• A Taxonomy of Graph Use Cases | Dan McCreary - Medium
• Graph database Use Cases | Max De Marzi - Slideshare

• 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

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• 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.

Cipher

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• 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

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• 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)

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• 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

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• 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).

• SQL ... and now GQL | Alastair Green
• What Is a Graph Database? | Amazon AWS
• Graph databases

Offering:

Amazon Neptune

...from Blazegraph

Dgraph

- written in Go

Semantic Knowledge Graph

• AI Powered Search | Trey Grainger
• Apache Solr ...open source enterprise search platform built on [https://www.w3schools.com/js/js_json_http.asp Apache Lucene™. Solr is highly scalable, providing fully fault tolerant distributed indexing, search and analytics. It exposes Lucene's features through easy to use JSON/HTTP interfaces or native clients for Java and other languages. The PyLucene sub project provides Python bindings for Lucene.
• Relevant Search: With applications for Solr and Elasticsearch | Doug Turnbull and John Berryman
• Changing Bits and Lucene in Action | Michael McCandless
• Wikis

Cybersecurity - Visualization

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• Cybersecurity
• Threat Hunting | Graphistry

Supercharged graph visualization for cyber security Cyber security analysts face data overload. They work with information on a massive scale, generated at millisecond levels of resolution detailing increasingly complex attacks. To make sense of this data, analysts need an intuitive and engaging way to explore it: that’s where graph visualization plays a role. Using KeyLines 3.0 to visualize your cyber data at scale During this session, Corey will show examples of how graph visualization can help users explore, understand and derive insight from real-world cyber security datasets. You will learn: - How graph visualization can help you extract insight from cyber data - How to visualize your cyber security graph data at scale using WebGL - Why KeyLines 3.0 is the go-to tool for large-scale cyber graph visualization. This session is suitable for a non-technical audience.

Finding Needles in a Needlestack with Graph Analytics and Predictive Models RSA Conference Kevin Mahaffey, Chief Technology Officer, Lookout Tim Wyatt, Director, Security Engineering, Lookout Good or bad? Security systems answer this question daily: good code vs. malware, legit clients vs. API abuse, etc. In the past, preset rules and heuristics have often been the first (and only) line of defense. In this talk, we'll share learnings you can take home from our experience using big datasets, graph analytics, and predictive models to secure millions of mobile devices around the world.

Applying graph visualization to cyber-security analysis Security information and event management/log management (SIEM/LM) evolve continuously to match new security threats. Nevertheless, these solutions often lack appropriate forensics tools to investigate the massive volumes of data they generate. This makes it difficult for security analysts to quickly and efficiently extract the information they need. Modeling this data into a graph database and adding a graph visualization solution like Linkurious on top of the company’s security dashboard can solve this problem. In this webinar, based on a real-world example, you will learn how Linkurious can help: detect and investigate visually suspicious patterns using the power of graph; perform advanced post attack forensics analysis and locate vulnerabilities; work collaboratively and locate suspicious IP’s using the geospatial localization feature.

RAPIDS Academy Graphistry Home

The Trick That Solves Rubik’s Cubes and Breaks Ciphers (Meet in the Middle)

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