What does graph have to do with machine learning, and data science? A lot, actually, and it goes both ways.

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. Which is not to say there’s lack of news on graph analytics, graph databases, and knowledge graphs – on the contrary.

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2020 has turned Graph Machine Learning into a celebrity of machine learning, argues Michael Bronstein. Arguably, he’s a celebrity of Graph Machine Learning himself. A pioneering and prolific researcher at Imperial College London, as well as the Head of Graph Learning Research at Twitter.

Bronstein sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. For a glimpse of Bronstein’s own work, check his Knowledge Connexions presentation “Deep Learning on Graphs: Past, Present, And Future”

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Machine learning can help bootstrap and populate knowledge graphs. The information contained in graphs can boost the efficiency of machine learning approaches. A panel featuring some of the world’s top experts in AI, coming from all sides of the spectrum, got together to discuss how AI + Knowledge are a match made in heaven.

Isabelle Augenstein, Nathan Benaich, Giuseppe Futia, Amy Hodler, Katariina Kari, and Fabio Petroni cover a number of ways graphs, AI and knowledge interact in this 2-hour tour de force.

For the Top Applications of Graph Neural Networks 2021, check our Sergey Ivanov’s quick introduction.

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Whether you are just getting started with graph-based data science and AI, or you are already advanced, Learning with the Machines is where you can get both inspiration and hands-on knowledge.

Aleksa Gordic will share How to get started with Graph Machine Learning. Bob van Luijt will explain How businesses apply AI-first solutions in production with the Weaviate Vector Search Engine. Paco Nathan will introduce Graph-Based Data Science, and then deliver a hands-on masterclass.

Tara Safavi will share her work on CoDEx: A Comprehensive Knowledge Graph Completion Benchmark, and Ashleigh Faith will show how to Add more context to machine learning, using taxonomies and knowledge graphs.

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When talking about AI, however, be it graph-based or not, there’s semantics involved. And not just in terms of defining what AI is.

AI is not just machine learning. Knowledge-based technologies are also AI. Lately, we started seeing more voices advocating for approaches to bridge the worlds of machine learning and knowledge-based technologies.

Gary Marcus, as he shared in a series of articles on ZDNet, and in his Knowledge Connexions keynote. Artur d’Avila Garcez and Luis C. Lamb, in their work Neurosymbolic AI: The 3rd Wave, Amit Sheth et. al. In Semantics of the Black-Box: Can Knowledge Graphs help make Deep Learning systems more interpretable and explainable?. And Frank van Harmelen et. al, who created Modular Design Patterns for Hybrid Learning and Reasoning Systems.

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The last few months have been good for graph databases. A graph database – Neo4j – made the Top 20 in DB Engines for the 1st time. Neo4j also announced general availability of its Aura managed cloud service on GCP, preview on AWS. Stardog announced its own cloud DBaaS too.

We had a series of funding rounds, and an upcoming IPO. TigerGraph scored $105M Series C, Katana Graph $28.5M Series A, Memgraph $6.7M and TerminusDB €3.6M. In the meantime Bitnine, makers of Agens Graph, is working on its IPO – the first in the market.

Last but not least, we had a round of new releases. AWS open-sourced a Graph Notebook to make working with graph databases easier. Franz Inc released AllegroGraph v7.1, Grakn Labs released Grakn v2.0 Alpha, , Nebula Graph released v2.0, Ontotext released GraphDB v9.6, RDFox released v.5.0, Stardog released v7.6, and TerminusDB released v4.0.

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It’s also been a very active period in terms of book publishing. Whether you’re interested in Graph Databases, Data Analytics on Graphs, or Knowledge Graphs and Semantic Technologies, there’s a new book out there for you.

Dave Bechberger and Josh Perryman published “Graph Databases in Action” on Manning. The book introduces graph database concepts by comparing them with relational database constructs. It promises to include just enough theory to get started, then progress to hands-on development.

Ljubiša Stanković et.al. published “Data Analytics on Graphs” on Now. The authors revisit graph topologies from a modern data analytics point of view, and proceed to establish a taxonomy of graph networks. With this as a basis, they show how the spectral analysis of graphs leads to even the most challenging machine learning tasks, such as clustering, performed in an intuitive and physically meaningful way.

William L. Hamilton published “Graph Representation Learning” on Morgan & Claypool. It aims to serve as If you need a jumping off point to learn more about Graph analytics and the use of graphs in machine learning, as these fields have exploded in the past few years.

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AI capabilities are improving daily, but understanding AI’s contextual data and problem-solving approaches is not easy. Graphs are a typically human way of navigating and accessing data. Graphs and AI bring out the best in the symbiosis of humans and systems.

To introduce Knowledge Graphs into Organizations you need to assess the right moment, the organization’s semantic maturity, and to overcome overcome segregation/specialization. It’s important to involve the right stakeholders, and develop KGs in an agile way.

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A common use case when working with graphs is converting tabular data to graphs, and adding semantics. From research efforts such as “Semantic Annotation for Tabular Data“, “Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets” and DAGOBAH, to Linked Data Wizard and implementations of W3C CSV on the Web recommendations, progress is being made.

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Another domain which historically has been prominent for experiments and applications of graphs is SEO. Schema.org had 2 new releases recently, v.11 and v.12, summarized by Aaron Bradley and Dan Brickley, respectively.

Search engines encourage content creators & developers to implement structured data. Structured markup vocabularies include RDFa & SchemaOrg. JSON-LD is more portable, easier to manage, and it has recently overtaken Microdata.

Knowledge Graphs have huge implications for SEO, content creation and digital marketing across the web. The Top 5 trends for SEO in 2021 include earning your presence in the Knowledge Graph, as it has tremendous impact across multiple platforms.

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Journalists, and data journalists, can use Crowdsourcing, Open data and Knowledge Graphs as a data source, by leveraging Wikidata. A new interconnected ecosystem for research is shaping up.

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More use cases for graphs, from exotic to everyday. Research claims an AI tool can distinguish between conspiracy theories and true conspiracies, using machine learning and graphs.

Netflix uses Knowledge Graphs for knowledge contained in the content universe. A self-supervised learning task is crafted. Random edges in the graph are selected to form a test set, and the rest of the graph is conditioned to predict missing edges.

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Wrapping up with more resources for graph-based machine learning. Jraph (pronounced “giraffe”) is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilites for working with graphs, and a ‘zoo’ of forkable graph neural network models.

Graph Neural Networks explained: Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts.

AutoML systems simplify and democratize AI. Tsinghua University developed an AutoML framework & toolkit designed for graph data and tasks. AutoGL handles all stages of graph learning, may reduce labour & bias in machine learning.