Nature Machine Intelligence - Designing methods to induce explicit and deep structural constraints in latent space at the sample level is an open problem in natural language processing-derived...

Graph Neural Networks can be used for a variety of applications but do you know what it takes to create a great recommendation system? Dive deep into the math of GNNs, implement a link prediction module and show everyone how stunning graph machine learning can be!

Change & Risk Require New Enterprise Tools As AI systems, bots (both digital and physical), AI leveraged smart digital identities, and IoT devices invade your enterprise, it increases the pace of change and risk. This brief article focuses on why you should be deploying TODA, EMS and graphs in your

SAN FRANCISCO, May 30, 2023 -- ArangoDB, the company behind the most complete graph data and analytics platform, today announced the GA release of

Ontotext receives growth funding to strengthen its leadership in the knowledge graph market and develop end-to-end vertical offerings.

The Graph Database Market size is driven by the increasing use of graph database tools and services based on artificial intelligence (AI) and the rising...

This week I was very fortunate to be invited to attend the honorary degree ceremony of Sir Tim Berners-Lee by the London School of Economics. The title of…

Don't make directed graphs undirected!  Introducing Dir-GNN, extending any spatial MPNN to handle directed graphs.  Preserving edge directionality is crucial…

News from eight different companies shows analytics, data management, streaming data and AI are increasingly intermingled, all in the cloud.

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The 2023 International Conference on Learning Representations is going live in Kigali on May 1st, and it comes packed with more than 2300 papers. Reasoning in Language Models, Diffusion, Self supervised learning for Computer Vision, Molecular Modeling, Graph Neural Networks, Federated Learning, and much more... Here's our guide to get you started. The role of conferences in the modern world of ML research has shifted. Previously seen as a platform for disseminating cutting-edge research, confere

Dublin, April 24, 2023 (GLOBE NEWSWIRE) -- The "Global Graph Analytics Market: Analysis By Component, By Deployment, By Enterprise Size, By Application,...

SALT LAKE CITY and TORONTO and MONTRÉAL , May 08, 2023 (GLOBE NEWSWIRE) -- Recursion (NASDAQ: RXRX), a leading clinical stage TechBio company decoding biology to industrialize drug discovery, today announced it has signed agreements to acquire two companies in the AI-enabled drug discovery space:

May 16, 2023 – GQL Status Update In February, 2023, the GQL standards committee (ISO/IEC JTC1 SC32 WG3) had a week-long meeting in Zeist, Netherlands, where we reviewed and accepted papers that completed the resolution of all of the GQL CD2 comments. The editors applied the papers, the authors

The global graph database market size is projected to grow at a compound annual growth rate (CAGR) of 22.5% to reach USD 5.1 billion in the upcoming five years. The market value was USD 1.9 billion in 2021.

We interviewed Varun Shenoy, a senior at Stanford who created a tool named GraphGPT.

By Dr. Verónica Espinoza, 2023 / Twitter @Verukita1

📢 the RDF-star Working Group at W3C has published 16 First Public Working Drafts, which represent the first milestone in the update of the #RDF and #SPARQL…

Nature Chemical Biology - Using a neural network trained on bacterial growth inhibition data, in silico prediction of molecules with activity against Acinetobacter baumannii led to the...

Graph ML News (May 20th) The NeurIPS deadline has passed so we could finally disconnect from the cluster, breathe in some fresh air and get ready for the supplementary deadline and/or paper bidding depending on your status. The Workshop on Mining and Learning with Graph (MLG) at KDD’23 accepts submissions until May 30th. This year KDD will feature both MLG and Graph Learning Benchmarks (GLB), so two more reasons to visit Long Beach and chat with the fellow graph folks 😉 CS224W, one of the best graph courses from Stanford, started publishing project reports of the Winter 2023 cohort: some new articles include solving TSP with GNNs, approaching code similarity, and building music recommendation system with GNNs. More reports will be published within the next few weeks. Weekend reading: DRew: Dynamically Rewired Message Passing with Delay feat. Michael Bronstein and Francesco Di Giovanni (ICML’23) Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs (ICML’23) Can Language Models Solve Graph Problems in Natural Language? On the Connection Between MPNN and Graph Transformer

Unlock Neo4j's power with Process Tempo by connecting non-technical users to graph data insights at scale

A deep dive from Contrary Research. AI has the potential to transform how humans process information in their own knowledge management systems.

Very cool paper showing that GNNs work in practical biological applications. It takes several years of effort to produce an experimental validation of computational results https://t.co/rmHiIFTOzn— Michael Bronstein (@mmbronstein) May 26, 2023

“Interesting rdf / graph darabase effort, do take a look /cc @wikidata”

“I am simultaneously excited & exhausted to announce that, 2 years later, our survey on GNNs for CO has been published @JmlrOrg (now at 61 pages of length!!!) 😅🚀 https://t.co/DBklZQPOAm Persistence paid off, guys! 😊 @chrsmrrs @69alodi @lyeskhalil @qcappart @didier_chetelat”

Graph ML News (May 27th): New Antibiotic found with Geometric DL, Differential Privacy, NeurIPS Submissions A new antibiotic abaucin is discovered by the power of Geometric Deep Learning! Abaucin targets a stubborn Acinetobacter baumannii pathogen resistant to many drugs. The new Nature Chem Bio paper (feat. Regina Barzilay and Tommi Jaakkola from MIT) sheds more light on the screening process and used methods. Stanford launches the online version of the flagship CS224W course of Graph ML. The 10-credit course is priced at $1,750 and starts on June 5th. The TAG in ML workshop on topology announced a new challenge: implementing more topology-enabled neural nets with the TopoModelX framework where top contributors will become co-authors of a JMLR submission. That’s a great option for those who’d like to start working with topological neural architectures! Vincent Cohen-Addad and Alessandro Epasto of Google Research published a post on differentiably-private clustering: introducing an approach for DP hierarchical clustering with formal guarantees and lower bounds, and an approach for large-scale DP clustering. The Weekend Reading section this week is brought to you by NeurIPS submissions, quite a number of cool papers: Link Prediction for Flow-Driven Spatial Networks - the work introduces the Graph Attentive Vectors (GAV) framework for link prediction (based on the labeling trick commonly used in LP) and smashes the OGB-Vessel leaderboard with a 10-points rocauc margin to the previous SOTA. Edge Directionality Improves Learning on Heterophilic Graphs feat. Emanuele Rossi, Francesco Di Giovanni, Fabrizio Frasca, Michael Bronstein, and Stephan Günnemann PRODIGY: Enabling In-context Learning Over Graphs feat. Qian Huang, Hongyu Ren, Percy Liang, and Jure Leskovec - a cool attempt to bring prompting to the permutation-invariant nature of graphs. Uncertainty Quantification over Graph with Conformalized Graph Neural Networks feat. Kexin Huang and Jure Leskovec — one of the first works on Conformal Prediction with GNNs. Learning Large Graph Property Prediction via Graph Segment Training feat. Jure Leskovec and Bryan Perozzi ChatDrug - a neat attempt at combining ChatGPT with retrieval plugins and molecular models to edit molecules, peptides, and proteins right with natural language. Extension of MoleculeSTM that we featured in the recent State of Affairs post. MISATO - Machine learning dataset for structure-based drug discovery - a new dataset of 20K protein-ligand complexes with molecular dynamics traces and electronic properties. Multi-State RNA Design with Geometric Multi-Graph Neural Networks feat. Chaitanya Joshi and Pietro Lio

Explore computational, algorithmic, and modeling challenges of analyzing massive graphs. Master machine learning techniques to improve prediction and reveal insights. Enroll now!