This month makes it 2 years since I was featured as a guest on a @neo4j YouTube livestream alongside @alexandererdl, where I discussed & demoed my analysis of the FIFA22 dataset. You can watch it here, in case you didn't see the live event 2 years ago 😉
#neo4j #FIFA22 #dataviz #graphdatabase #graphDataScience
https://www.youtube.com/live/ZADwMoBJ6GQ?si=VG_HnFCvZRdYGH1h
Lately I’ve become intrigued about the published research + open source code for a relatively specific topic: generating graphs to use for inference.
Here is a comparison of five research projects circa 2019-2024 which explore different ways of generating graphs to use for inference.
Visualizing a document vector embedding (index) as a clustered k-nearest-neighbour graph is so insightful. You see which of those 20k+ arxiv papers are close together by the text embedding of their abstract. #neo4j #graphdatascience and you can expand from the vector search to the context of your documents (authors, venues, categories and related information) to e.g. power a #rag application.
"Graph Levels of Detail"
https://blog.derwen.ai/graph-levels-of-detail-ea4226abba55
We're circulating for review this survey of methods for abstraction layers in knowledge graphs. This covers mathematical approaches from several areas, so it's important to hear back whether any of the descriptions are misrepresented. Also, is this kind of work interesting for your organization?
Here are slides for my recent talk -- I really appreciated the opportunity to present at K1st World, JK2K's meetup in DC, and Corunna Innovation Summit, and the many interesting discussions!
"Language, Graphs, and AI in Industry"
https://derwen.ai/s/mqqm
This links to a directory of resources related to graph resources:
https://derwen.ai/graph/
Also, join our Graph Data Science group on LinkedIn:
https://www.linkedin.com/groups/6725785/
A summary of links for the emerging intersection of graph ML and uses for LLMs
https://blog.derwen.ai/visual-missives-from-the-latent-space-2023-10-16-d4bfa944b86c
New 3.2.5 release of `pytextrank` is available on PyPi.org now. Kudos to @hedgeknight @normanbaatz
https://pypi.org/project/pytextrank/3.2.5/
#python #nlp #opensource #graphDataScience
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
"GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks"
https://arxiv.org/abs/2302.08043
Zemin Liu, et al. (2023-02-16)
A novel pre-training and prompting framework on graphs, for the "pre-train, fine-tune" and "pre-train, prompt" approach to working with GNNs
From the excellent "Prompt Engineering Guide" by Elvis Savaria
https://github.com/dair-ai/Prompt-Engineering-Guide
exploring diffusion in GraphML, in drug discovery:
"Denoising Diffusion Generative Models in Graph ML"
https://towardsdatascience.com/denoising-diffusion-generative-models-in-graph-ml-c496af5811c5
Michael Galkin
Towards Data Science (2022-11-26)
a very good introduction to GraphML:
"Introduction to Graph Machine Learning"
https://huggingface.co/blog/intro-graphml
Clémentine Fourrier, Hugging Face
excellent visualizations about graph algorithms:
"The hidden beauty of the A* algorithm"
https://www.youtube.com/watch?v=A60q6dcoCjw
from Luis Natera's newsletter https://buttondown.email/natera
