The Year of the Graph Newsletter, keeping track of all things Graph, year over year, is back after a long hiatus. Read on to learn more about how the evolution of the newsletter follows the evolution of the domain and how to be involved, as well as industry news and analysis hot off the press: The Evolution of Graph and the Year of the Graph Newsletter Knowledge Graphs are in conversational mode Graph Database growth going strong through the Trough of Disillusionment Graph AI is hot in research and making inroads into the industry Graph Analytics go big and realtime Foursquare Graph, the first Geospatial Knowledge Graph The Personal Knowledge Graph book, the first book on PKGs The Evolution of Graph and the Year of the Graph Newsletter A lot of water has flowed under the bridge since . Two years is a long time in technology, especially for something as diverse and rapidly evolving as graphs. Here is a letter from the editor with a recap of what happened during the hiatus, and what’s coming next. the last issue of the YotG Newsletter In terms of the evolution of the Graph landscape, a few things have changed. The Newsletter may have taken a break, but keeping track of all things Graph Year over Year never stopped. The following sections assess where Knowledge Graphs, Graph Databases, Graph Analytics, and Graph AI are today and where they’re headed to. As you may know, if you have been following the YotG Newsletter for a while, the editor here would be me – . I’ve been into graphs in one way or another since the early 2000s. I’ve built Graph Database prototypes, was a part of award-winning Graph R&D, and led teams using Graph Databases in production. George Anadiotis In the last few years, I’ve been active as an analyst and writer covering Graph technology. I participated in the pivotal W3C Web Standardization for Graph Data initiative in Berlin as an independent expert and I have authored and numerous articles on Graph Databases and beyond. I have also been organizing and compiling the YotG newsletter. And that’s not even all I do. 3 reports Connected Data World That’s a lot, so something’s got to give. This is what induced the hiatus, and why the YotG Newsletter is changing. The newsletter has historically been a long list featuring short excerpts from all-around industry news on all things and , compiled by myself exclusively. Graph Analytics, Graph Databases, Knowledge Graphs, Graph AI, Data Science That was not sustainable. Going forward, the newsletter will be shorter, featuring longer excerpts from my own work on Graph, as well as a brief editorial and some analysis. Updates from the industry and research are still published on the YotG and accounts. Feel free to follow those for the latest news, and reach out if you are interested in contributing. Twitter LinkedIn The has been revamped. It looks better and is easier to navigate, but the biggest change is that YotG’s list of resources on all things Graph Analytics, Graph Databases, Knowledge Graphs, and Graph AI are now available under the . Feel free to browse, search, learn, and share. YotG web site corresponding sections Fun fact #1: Classifying the huge backlog of 3K+ resources was done with a little help from ChatGPT. Last but not least, you can always contact the YotG if you want to: Ask a question or share an invite Submit news items or otherwise contribute Inquire about sponsored content or professional services Knowledge Graphs are in conversational mode The emergence of ChatGPT and the subsequent hype have made people across different domains think about ways they could use Large Language Model (LLM) technology to their benefit. Knowledge Graphs are no exception. As : Dean Allemang puts it “Quite a lot [of what I’ve been reading about ChatGPT and LLMs] has to do with how the weaknesses of LLMs can be addressed by the use of [people’s] favorite technology. This is probably because I hang out in groups where people are interested in their own pet technology. Since I have been an advocate for Knowledge Graph technology for many years, I am as guilty of this as the next writer, of thinking that the key to making LLMs useful is to combine them with Knowledge Graphs”. There has been no shortage of ideas on how to combine Knowledge Graphs with LLMs. Most of them fall under one of three categories: using an LLM to create a new Knowledge Graph, using an LLM to access an existing Knowledge Graph, or using a Knowledge Graph to augment an LLM. There are numerous ongoing efforts and experiments, of which we are going to share just a few. creates a knowledge graph of the connections between people and proper nouns contained in input sentences. GraphGPT uses and was created by , a researcher at Stanford University. GraphGPT GPT-3 Varun Shenoy Peter Lawrence shows . To illustrate this, he uses an RDF knowledge graph of a process plant, the core of a Digital-Twin, to prompt or fine-tune OpenAI’s GPT LLM. how a knowledge graph can prompt or fine-tune a LLM enabling users to ask their questions Fun fact #2: we . called this back in 2020 Tony Seale, Knowledge Graph Engineer at UBS, suggests . This includes alignment with an ontological worldview, i.e. leveraging concepts defined in a Knowledge Graph’s ontology to constrain the language model. harnessing the power of Knowledge Graphs for Language Model governance Graph Database growth going strong through the Trough of Disillusionment One of the first items in the YotG graph collection of graph resources was a . Back in early 2018, the report valued the Graph Database market size at $700 Million in 2017 and projected it to reach $2,4 Billion by 2023, at a Compound Annual Growth Rate (CAGR) of 24%. Graph Database market forecast report by Markets and Markets That’s pretty substantial and pretty optimistic, but has it actually materialized? In the most recent , the Graph Database market size is estimated at $1,9 Billion in 2021 and projected it to reach $5,1 Billion by 2026, at a CAGR of 22,5%. update of this report by Markets and Markets in 2022 Even though it’s not really possible to verify those estimates except against previous estimates, in that respect the projections seem pretty much on the mark. CAGR has declined somewhat but remains high and the market is still poised for growth. But it has not been uninterrupted growth all the way. If we take Gartner’s hype cycle at face value, . Graph Databases are probably still going through the Trough of Disillusionment That means that . In addition, the recent and ongoing economic downturn has not left the graph database market unaffected. Some vendors were part of the wave of layoffs, others have , funding has slowed down and the economic climate is reportedly having an impact on sales. the utility of graph databases may be questioned changed leadership But that does not mean progress is stalling either. has not evaporated – far from it. And Graph Databases keep working on becoming more accessible, adding cloud services, visual interfaces and – you guessed it – conversational interfaces too. Investing in Graph Databases RDF and LPG Graph Databases are converging through , the RDF update that enables RDF to operate as property graphs, and , the new international standard query language that is expected to be generally available in 2024. RDF-star GQL If you are looking to decipher Graph Databases and make a decision on what is the right solution for you, check out the for a detailed vendor-by-vendor analysis. YotG Graph Database report Graph AI is hot in research and making inroads into the industry When YotG first included Graph AI in its coverage, Graph AI was considered exotic. While Graph AI is still generating lots of research output, it’s also making inroads into the industry. Even back in 2019, . That trend has been accelerating; for example, check out this , where Graph Representation Learning, Geometric Deep Learning and its applications for molecular modeling and physics are key themes. Graph AI was a considerable part of new research in top AI venues Guide to ICLR 2023 Transformers, the architecture behind LLMs, can also be viewed as a Graph Neural Network. Graph Convolutional Networks (GCNs) combine deep learning with feature diffusion to produce useful node embeddings. And Geometric Deep Learning provides a common blueprint allowing to derive from first principles neural network architectures as diverse as CNNs, GNNs, and Transformers. Somewhere in between research and industry is the concept of . Tailored for large incomplete graphs and on-the-fly inference of missing edges using graph representation learning, neural reasoning promises to maintain high expressiveness and support complex logical queries similar to standard graph query languages. Neural Graph Databases In its short history, Graph AI has had a strong footing in the pharma industry with applications in drug discovery. Recursion, a major player in drug discovery, has just : Valence Discovery (Mila, Montreal) for $47.5M and Cyclica (Toronto) for $40M. Beyond pharma, . acquired two startups in order to leverage their Graph AI-powered intellectual property Graph AI is also used at the likes of Airbnb Graph Analytics go big and realtime Graph Analytics is coming on its own. There are a few noteworthy pieces of evidence to back up this claim. First, there are now multiple market forecast reports that consider Graph Analytics a market of its own. For example, : Research and Markets states that “The global graph analytics market in 2022 was valued at US$1.14 billion. The market value is anticipated to grow to US$6.90 billion by 2028. The market value is expected to grow at a CAGR of 34.80% during the forecast period of 2023-2028”. Second, there are more and more resources around graph analytics, and they generate a lot of interest. Some recent examples: Maryam Miradi’s collection of . Amy Hodler’s . 25 top Python libraries, types, algorithms and techniques for Graph Analytics Graph Analytics talk for G-Research in London More than anything else, however, it’s the fact that Graph Analytics are having an impact in the real world. From ESG to Customer 360, finance, supply chains, retail and anti-fraud, Graph Analytics is gaining adoption and becoming a significant part of vendor revenue. The importance of Graph Analytics may make it a central part of . Databricks’ strategic decisions going forward too In industry use cases, the requirements often dictate processing large volumes of data in near-realtime. Graph analytics are growing in that direction, as exemplified by the likes of and . There are now , too. Plus, . We’re watching this space and we’ll be back with more details. PayPal LinkedIn solutions for streaming Graph Analytics Graph Analytics are powering cybersecurity unicorns Foursquare moves to the future with a Geospatial Knowledge Graph If the name rings a bell, it means you were around in the 2010s. Your only resort to plausible deniability would be if you are a data professional – although that’s not an either/or proposition. Foursquare In the 2010s, Foursquare was a consumer-oriented mobile application. The premise was simple: people would check in at different locations and get gamified rewards. Their location data would be shared with Foursquare and used for services such as recommendations. and Yelp got the lion’s share of that market, but Foursquare is still around. In addition to , Foursquare’s data is used to power the likes of Apple, Uber and Coca-Cola. Facebook having 9 billion-plus visits monthly from 500 million unique devices Recently the company announced Foursquare Graph, what it dubs the industry’s first application of graph technology to geospatial data. Heroes journey: Notes towards a Personal Knowledge Graphs Book The last couple of years has seen the emergence of a new type of Knowledge Graph: Personal Knowledge Graphs. Knowledge Graph definitions abound. Personal Knowledge Graph definitions are in flux. Previously, . That has ,. More importantly, however, Personal Knowledge Graphs power a booming ecosystem of real-world tools focused on end-users, with data sovereignty and note-taking as key elements. Personal Knowledge Graphs were academically defined as graphs containing facts about a person, held by a 3rd party recently changed Note-taking is a timeless practice. Over time, a multitude of software tools have been developed to assist with note-taking. Applying Graph metaphors and principles in the personal information management domain and note-taking results in what we call Personal Knowledge Graphs. The first book on Personal Knowledge Graphs is a journey of exploration and mapping of emergent practices and tools. The is a common template of stories that involve a hero who goes on an adventure, is victorious in a decisive crisis, and comes home transformed. Writing a book is like a journey too. Writing the first Personal Knowledge Graphs book involved more than one hero and a few crises. hero’s journey Also published here. Would you like to receive the latest Year of the Graph Newsletter in your inbox? Easy – just signup . 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