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Graph-Based Data Science, Machine Learning, and AI

Introduction

Over the last few years, we have seen what was once a niche research topic —graph-based machine learning—snowball. The Year of the Graph was among the first to take stock, point towards this development, and recognize graph-based AI as a key pillar for future development in the field. 

In this edition of the YotG Newsletter, we highlight resources focused on graph-based machine learning and data science. This is not to say that there's a lack of news on graph analytics, graph databases, and knowledge graphs — rather, the opposite is true.

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Graph Therapy: The Year of the Graph Newsletter, June/May 2020

Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted. 

An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way. 

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Knowledge Graphs Power Scientific Research and Business Use Cases: Year of the Graph Newsletter, April/March 2020

Is there life after COVID-19? Of course there is, even though it may be quite different, and it may be hard to get there. But there's one thing in common in the "before" and "after" pictures: science and technology as the cornerstones of modern society, for better or worse.

We have argued before that Knowledge Graph is a technology that enables other technologies to accelerate their growth, and it also enables humans to take stock of their own knowledge. This is why the future is Knowledge Graph.

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See What’s New in Neo4j 4.0

If you’re not directly plugged in to Neo4j-specific news, you may not have seen the recent splash of all-new features in Neo4j’s latest database version release. Or, perhaps you saw it but it wasn’t clear how it could benefit or help you in the work that you are doing.

This is an exciting release, most of which is based around the evolution of the Neo4j database into a full database management system (DBMS)! There are a variety of new features that go along with this development and assist users and teams in managing multiple graphs.

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15 Rules of a Native Graph Database

15 rules of a native graph database

Like a complex system grid or an air-traffic-control map, a graph database is represented as a network of nodes and connections called a labeled property graph. The nodes, which appear as circles or squares, represent entities such as people, products, companies or orders.

In Neo4j, the connections between database nodes are called relationships, and those relationships are as important as the nodes they connect.

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Getting Started With ReGraph — The Graph Visualization Toolkit for React

At Cambridge Intelligence, we recently launched an Early Access Program (EAP) for ReGraph, our brand new graph data visualization toolkit for React developers. The response from evaluators has been fantastic, and now we’re inviting more organizations to join the ReGraph EAP.

To help you get started with ReGraph, this step-by-step tutorial covers everything you need to know. Once we’ve created our visualization in a React app, we’ll load an example network of suspected terrorists and show how easy it is to apply the key analysis techniques your users need to uncover threats.

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Applying Graph Analytics to Game of Thrones

In this post, we review how organizations are integrating graph transactions and analytic processing and then dive deeper into graph algorithms. We’ll provide examples of using graph algorithms on Game of Thrones data to illustrate how to get started. Note that portions of this content have been taken from our O’Reilly book, Graph Algorithms: Practical Examples in Apache Spark and Neo4j, which you can download for free.

Neo4j provides native graph storage, compute, and analytics in a unified platform. Our goal is to help organizations reveal how people, processes, locations, and systems are interrelated using a connections-first approach. The Neo4j Graph Platform powers applications tackling artificial intelligence, fraud detection, real-time recommendations, and master data.

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Text-Mined Knowledge Graphs — Beyond Text Mining

Text is the medium used to store the tremendous wealth of scientific knowledge regarding the world we live in. However, with its ever-increasing magnitude and throughput, analyzing this unstructured data has become an impossibly tedious task. This has led to the rise of Text Mining and Natural Language Processing (NLP) techniques and tools as the go-to for examining and processing large amounts of natural text data.

Text-Mining is the automatic extraction of structured semantic information from unstructured machine-readable text. The identification and further analysis of these explicit concepts and relationships help in discovering multiple insights contained in text in a scalable and efficient way.

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Mobile Experiences at Scale: 5-Minute Interview with James Gray [Video]

"Literally within minutes or seconds, customers generate mobile device audiences at scale," said James Gray, Director of Product for Big Data at Phunware.

Phunware's mobile applications platform allows some of the biggest brands in the world to deliver great mobile experiences and reach mobile app users with relevant content. To achieve this at scale, Phunware uses Neo4j as the engine to power a knowledge graph connecting more than a billion nodes.

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Financial Regulations: Neo4j Risk Management Platform [Infographic]

Financial regulation in the U.S. has become a complicated and fragmented system, but why?

Various authorities creating compliance laws include a cast of six federal regulating agencies: The Federal Reserve, the Office of the Comptroller of the Currency, the National Credit Union Administration, the Federal Deposit Insurance Corp., the Securities and Exchange Commission, and the Office of Thrift Supervision. There are also state bank regulators, adding even more agencies (and regulations) into the swirling, whirling mix you so desperately want (nay, need) control of.

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On Evolution of Database Languages, Part 3

The article “Abstraction Tiers of Notations, Part 1” introduced abstraction tier concept, and in the article “Birth of New Generation of Programming Languages? Part 2,” I have tried to apply it to the evolution of the general-purpose programming languages. However, this framework is applicable to domain-specific languages as well. Let’s consider one of the most popular domains, where DSLs are widely used: data manipulation languages.

Current State

Firstly, let’s briefly examine current technologies available on the market. We will consider only employed abstraction tiers of data manipulation language for the database technologies while ignoring other aspects like distribution models, transaction support, or performance. While these aspects are very important for technology selection, they are orthogonal to the supported abstraction tiers.

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Social Power, Operational Ease: 5-Minute Interview

“We were able to go from 48 Cassandra instances to three Neo4j instances. Going into the project, we weren’t sure whether that was going to be possible,” said David Fox, Software Engineer at Adobe.

Adobe runs Behance, a social network where digital artists share portfolios, learn from one another, and showcase their work. The activity feed is a key feature of the site and part of the home page. When long-time Neo4j community member David Fox joined Adobe, he saw how a graph database could make this feature sing.

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Graph Algorithms in Neo4j: All Pairs Shortest Path

Graph algorithms provide the means to understand, model, and predict complicated dynamics such as the flow of resources or information, the pathways through which contagions or network failures spread, and the influences on and resiliency of groups.

This series is designed to help you better utilize graph analytics and graph algorithms so you can effectively innovate and develop intelligent solutions faster using a graph database.

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7 Great 2018 Advancements in Enterprise Knowledge Graphs

While the term “Knowledge Graph” is relatively new (Google 2012), the concept of “representing knowledge as a set of relations between entities — forming a “graph” — has been around for much longer.

2019 marks, for example, the 20th anniversary of the publication of arguably the first open standard for representing “Knowledge Graphs” designed with web distribution and scale in mind (The W3C RDF standard).