Chapter 10: Graph Data Science & Machine Learning

Maximizing Machine Learning with Graph Neural Networks (GNNs)

Unlock the potential of machine learning in just 6 minutes with our video, 'Maximizing Machine Learning with Graph Neural Networks (GNNs).' Explore the fundamentals of GNNs through real-world applications that can be applicable in any industry. #MachineLearning #GraphNeuralNetworks #AI #GNNs #DataScience

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Graph+AI Breakout: Using Graph Machine Learning To Detect Complex Attacks

Modern cyberattack campaigns target a company’s digital assets and have strong economic incentives. Imagine one day a DevOps developer machine is compromised. Does it just happen to be involved in an attack not targeted to the company? Or is it something aimed at the company’s critical digital assets and thus 1000x more dangerous? The distinction...

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Graph+AI Breakout: MLOps for Graph Machine Learning

Established MLOps pipelines include capabilities like feature stores and model explainability that do not easily translate to graph machine learning models built around graph databases. In this talk, you'll see an example of a working MLOps pipeline for graph machine learning, and hear about some of the challenges associated with building that...

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Graph+AI Panel: Delving into the Deep Benefits of the Graph Deep Learning Library

Go beyond traditional machine learning and understand why it's important to ‘go graph’ in this panel discussion. Explore why graph learning libraries came into existence to support graph-based machine learning, one of the driving forces behind TigerGraph's recent development of its Machine Learning Workbench. The panel will also discuss how graph...

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Graph+AI Breakout: When Can Graph Neural Networks Work?

Graph Neural Networks (GNNs) have shown their power in graph representation learning, advancing various real-world applications in many domains such as biology and healthcare. As a result, a large number of GNNs have been developed in recent years. However, graphs in reality can be very diverse and lack understanding of when GNNs can work in...

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Graph+AI Breakout: Graph Data Science with TigerGraph Graph Algorithms

Graph algorithms are very helpful to analyze connected data. In this talk, we will introduce TigerGraph Graph Data Science algorithm technologies, showing you how to set up TigerGraph studio in a data science project with abstract ontologies as well as introducing GSQL query language and the Graph Data Science Library.

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Graph+AI Breakout: Graph Machine Learning: From Classical Approach to Graph Neural Network

Over the last few years, we've seen a rise in graph algorithms in a lot of use cases. One overlooked problem is that we lack a map to orient ourselves in this changing technological world. In this talk, we'll explain the logical steps and algorithms used for graph-based machine learning paths. You'll go on a journey starting with classical machine...

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Echtzeit-Digital Twin Basierend Auf Graph Analytics: Optimal In Unsicheren Zeiten Reagieren

Mit einer modernen Graph Analytics Plattform wie TigerGraph können Unternehmen die Vision eines digitalen Zwillings, der die gesamten vernetzten internen und externe Datenquellen abbildet, erstellen und Analysen und Reports in Echtzeit bereitstellen. Sei es für alle internen und externen Datenquellen wie z.B. ihrer Supply Chain-Funktionen, ihrer...

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Million Dollar Challenge: 2nd Place Most Ambitious Entry - TigerLily

Drug-drug side-effects are rarely tested directly in the pharmaceutical development process. Our system can generate in silico indications of drug-drug interactions using graph machine learning.

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Million Dollar Challenge: Most Popular Entry - Multimodality Cancer Disease Link Prediction

Through the power of graphs and machine learning, we aim to uncover new disease links and help researchers better prepare for future pandemics or emerging diseases.

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ML Workbench Overview

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TigerGraph Machine Learning Workbench Overview

0:00 Introduction 0:10 One Sentence Overview 0:23 Why Graph Machine Learning 2:17 Who's using Graph + Machine Learning 5:04 TigerGraph ML Workbench Overview 6:14 TigerGraph ML Workbench Architecture 7:54 Outro

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TigerGraph Machine Learning Workbench GraphSAGE Tutorial

Here we'll walk through how to run a sample GraphSAGE project within the TigerGraph Machine Learning Workbench.

00:00 Intro 0:25 Setting up the ML Workbench 0:42 Installing Dependencies 1:05 Connecting to TigerGraph 2:40 Data Splitting 4:17 Whole Graph Training 7:19 Neighborhood Subgraph Training 9:45 Model Testing 10:23 Outro

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TigerGraph - Graph for All Million Dollar Challenge

TigerGraph launched the Graph for All Million Dollar Challenge on February 9, 2022 https://www.tigergraph.com/graph-for-all/ as a global search for innovative ways to harness the power of graph technology and machine learning to solve real-world problems. The challenge brings together brilliant minds to build innovative solutions to better our...

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Graph For All Million Dollar Challenge - Monitor Impact Of Climate Warming In The Arctic

The Graph For All Million Dollar Challenge is a global search for innovative ways to harness the power of graph technology with machine learning to solve real problems. You can win up to $250,000 USD and make an impact in one of our challenge areas.

Alexey Portnov is a marine geoscientist who uses geophysical methods to address global geological...

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Problem Statement: Graph For All Million Dollar Challenge - Foster Critical Thinking

The Graph For All Million Dollar Challenge is a global search for innovative ways to harness the power of graph technology with machine learning to solve real problems. You can win up to $250,000 USD and make an impact in one of our challenge areas.

María Laura Garcia is the Founder and President of GlobalNews®️ Group, the premier source of media...

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Graph For All Million Dollar Challenge - Predict Next Wave Of Covid

The Graph For All Million Dollar Challenge is a global search for innovative ways to harness the power of graph technology with machine learning to solve real problems. You can win up to $250,000 USD and make an impact in one of our challenge areas.

Dave DeCaprio Dave is a co-founder and the Chief Technology Officer at ClosedLoop.ai, healthcare...

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Graph For All Million Dollar Challenge - Find Novel Drug Treatments

The Graph For All Million Dollar Challenge is a global search for innovative ways to harness the power of graph technology with machine learning to solve real problems. You can win up to $250,000 USD and make an impact in one of our challenge areas.

Dave DeCaprio Dave is a co-founder and the Chief Technology Officer at ClosedLoop.ai, healthcare...

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Graph Gurus Data Science Library Series Part 2: Recent Additions

Ready to grow your graph data science skills? In this webinar series, we show you how to use the 50+ algorithms in TigerGraph's Graph Data Science Library that span Dependencies, Clustering, Similarity, Matching/Patterns, Flow, Centrality, and Search to develop your next graph solution.
Register to see the full series -...

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Graph Gurus Data Science Library Series Part 1: Fundamentals

Ready to grow your graph data science skills? In this webinar series, we show you how to use the 50+ algorithms in TigerGraph's Graph Data Science Library that span Dependencies, Clustering, Similarity, Matching/Patterns, Flow, Centrality, and Search to develop your next graph solution.
Register to see the full series -...

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Hands on with the TigerGraph Graph Data Science Library and Python

Learn how to utilize TigerGraph's Graph Data Science Library to perform in-database machine learning and feature extraction through hands-on examples. We will use TigerGraph and Python with a variety of hands-on examples to help gain an understanding of how different algorithms help solve business problems.

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UCSD: Integrate TigerGraph with Jupyter Notebooks Part 2

Part 2 of a 3 part series for UCSD: Integrate TigerGraph with Jupyter Notebooks (pyTigerGraph, TigerGraph, and Google Colab)

Survey https://docs.google.com/forms/d/e/1FAIpQLSf8VIl6ts8gNN8XIYwLyq8xfpGUJTZTPQG4vX3elgsA_RFTQw/viewform?usp=sf_link

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Graph Gurus 55: Improving Cyber Threat Detection with Graph Analytics

The sophistication of cybercriminals is increasing relentlessly. Accenture found that 68% of business leaders feel their cybersecurity risks are increasing. More and better technologies are required to detect attacks and prevent them. In this live webinar, we’ll discuss:

➡️ How graph analytics, machine learning, and visualizations can directly...

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Kampf gegen Finanzbetrug: Graph Analytics und Machine Learning als entscheidende Technologie!

Presenter : Mario Werner, Regional Manager DACH

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PartnerGraph Webinar - Expero - Accelerating Supply Chain Optimization

Accelerating Supply Chain Optimization with Machine Learning, Graph Analytics and Visualization

Recorded June 17, 2021

Supply chains have evolved significantly for manufacturers in all industries, and especially automotive, pharma, retail, and tech. This has created an imperative for real-time systems that enable predictive planning and...

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Graph Shortest Path & Centrality based on Airport Dataset

By Qi Chen and Frances Keung

First Place Winner

This shortest path application is a convenient visualization tool for backpackers or common travelers to search the airport information around the world. They can obtain airport details or compare the different path between stops by visualization. These functions are simple but untapped. The...

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Graph + AI Summit Spring 2021

Graph + AI Summit is a virtual event for data analytics and AI professionals.

It is focused on accelerating analytics and AI with graph algorithms.

Register here at www.graphaisummit.com

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Guarding Against Cyberthreats With Graph Database and Machine Learning

Presented by Abhishek Mehta and Gaurav Deshpande on AI4 Cybersecurity Virtual Conference.

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Data Science Salon 2020: Driving eCommerce Revenue and Profits With Personalized Recommendations

COVID-19 pandemic has raised the stakes. To survive and thrive, all organizations must deliver a better customer experience online. Companies such as Nike and Walmart are reaping the rewards, taking market share and accelerating the growth of their eCommerce channels. Improving customer experience in a digital world requires connected intelligence...

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Graph Gurus 44: Building The Next Generation Customer Experience With Graph And Machine Learning

The COVID-19 pandemic has raised the stakes - to survive and thrive, all organizations must deliver a better customer experience online. Companies such as Nike and Walmart are reaping the rewards, taking market share and accelerating the growth of their eCommerce channels. Improving customer experience in a digital world requires connected...

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Graph + AI 2020 Recap - Investor Executive Roundtable - New Opportunities Unlocked By Graph + AI

The combination of graph algorithms and analytics with AI unlocks a new world of opportunities for investors. Join us as we explore it with the leading venture capitalists from the Silicon Valley teaming up with the most innovative companies.

To watch these full videos on-demand register at graphaiworld.com

Step 1: Log into the Graph + AI...

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Graph + AI World 2020

Accelerate AI Machine Learning with Graph Algorithms. Sign up here: https://www.tigergraph.com/graphaiworld/

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Graph Gurus 32: Using Graph Algorithms for Advanced Analytics - Part 5 Classification

By watching this webinar you will:

-See how similarity algorithms are used to calculate "distances" between entities. -Learn what data scientists mean when they say Labels and Training. -Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to...

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Graph Gurus 30: Using Graph Algorithms For Advanced Analytics - Part 4 Similarity

This webinar explores structural similarity algorithms which measure how similar are the patterns of interconnection of a given pair of entities. By watching this webinar you will:

➡ See use cases where graph pattern and subgraph similarity are valuable ➡ Weigh the pros and cons of different similarity algorithms ➡ Be able to run and tailor GSQL...

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Graph Gurus 29: Using Graph Algorithms for Advanced Analytics Part 3 - Community Detection

By watching this webinar you will:

-Hear about use cases for community detection graph algorithms -Learn how to select the right algorithm for your use case -Be able to run and tailor GSQL graph algorithms

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Graph Gurus 28: An In-Database Machine Learning Solution For Real-Time Recommendations

In this Graph Gurus episode, we:

-Review multiple widely-used recommendation methods -Introduce the concept of in-database machine learning -Present an in-database machine learning solution for a real time recommendation system

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Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics - Part 2 Centrality

By listening to this on demand webinar you will:

-Hear about use cases for centrality graph algorithms -Learn how to select the right algorithm for your use case -Be able to run and tailor GSQL graph algorithms

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Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1 - Shortest Paths

This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.

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Graph Gurus 22: Guard Against Cyber Security Threats With TigerGraph

By watching this webinar you will learn how to:

-Detect and mitigate attacks against a firewall with unprecedented accuracy -Identify and block devices used in denial of service attacks -Build “footprint” profiles that can be used for machine learning.

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Healthcare - Referral Networks StarterKit

TigerGraph Healthcare - Referral Networks, Hub and Community Detection StarterKit

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Healthcare - Referral Networks StarterKit

TigerGraph Healthcare - Referral Networks, Hub and Community Detection StarterKit

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Graph Gurus Episode 5: Finding Hubs of Influence - Implementing PageRank with a Graph Database

This major graph analytics algorithm needs little introduction, as Google has made it a household term: PageRank. Here we are looking for the most influential or popular members of a particular group - whether its a web page that is referred or linked to by the highest number of pages, subscribers driving the highest volume of calls in a network,...

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Webinar: TigerGraph for eCommerce

Increasing Revenue & Customer Loyalty With A Real-Time Product Recommendation Engine

Learn how TigerGraph's real-time deep link analytics powered by a highly scalable graph database, combined with machine learning, is allowing eCommerce companies to:

-Identify what a customer is looking for at a particular business moment

-Recommend related...

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