#graphlearning search results
🚀 Excited to share our paper got accepted at DiffCoAlg@NeurIPS 2025 @diffcoalg !🎉 🙏 Thanks to @shrutimoy, Binita Maity, Anant Kumar, @adagu & @cse_iitgn . #NeurIPS2025 #GNN #GraphLearning #AIResearch

Join us for the Stanford Graph Learning Workshop 2025! 🗓️Oct 14, 2025 📍Stanford University 🧠Topics: Agents, RFMs & LLM Inference. Save your spot to explore the future of #AI, #LLMs and #GraphLearning with leading experts. Register now: snap.stanford.edu/graphlearning-…

🚨 BREAKTHROUGH in Graph Learning! What if each node in your graph could plan, reason, and act like a mini-agent—powered by an LLM? 🤯 That’s exactly what ReaGAN does. And it might just outsmart classic GNNs. Let me explain 🧵👇 #AI #GraphLearning #LLM #MachineLearning

AIHybets builds a live, evolving Semantic Graph— Each node = a signal, insight, term, or prompt. You don’t interact with the graph. You become part of it. #SemanticAI #GraphLearning
📢 New paper: Robustness Potential Explorer (RPE) A 3-part framework to visualize & predict network robustness. ✅ Outperforms CNN/GNN methods 🔍 RPE-F | RPE-V | RPE-P #AI #NetworkRobustness #GraphLearning (content generated by Copilot) ieeexplore.ieee.org/abstract/docum…
@scne just presented their latest work at @ACMRecSys #GraphLearning session. This work, co-authored by @dmalitesta @alberto_mancino @walteranelli @TommasoDiNoia Explores the relationship between topological datasets characteristics and GNN based recommender systems.

Highlights of today's Preconference Tutorials for #iccins2023 #mylavaram Dr.Tushar Semwal and Mr.Nahar Singh delivered the talks on #graphlearning #generativeai The Tutorials Chair and the committee members felicitated the resource persons.




We invite you to read our full TMLR paper (Feb 2025) 👉 [openreview.net/forum?id=HjpD5…] and join the discussion on how these insights could reshape the design of self-supervised learning frameworks in graph data! #GraphLearning #SSL #ContrastiveLearning
Get news and updates from Kumo AI. We're bringing the most powerful #GraphLearning approaches, proven in research, to the enterprise. hubs.ly/Q02g3LXK0

A really good webinar on how effective #GraphLearning can be for your customer growth initiatives: #LTV #Churn #CustomerRetention hubs.ly/Q01X21pB0

Our next talk will be given by @Pseudomanifold on "Vertex, Edge, Clique: What's in a Graph?". Join us on Nov 20 (Wed) at **2pm** (CET). Check out dsiseminar.github.io for details. #graphlearning

Check out the latest survey on Large Language Models for Graphs! Explore the integration of LLMs with graph learning techniques, analyzing design frameworks and potential research avenues. Access the full article at bit.ly/3QMvdH5. #graphlearning #largelanguagemodels
📊#GNN case study: 73% better predicting “next best" offers at an online bank in 3-days of modeling across 3B records. Learn more: hubs.ly/Q02dclSP0 #PredictiveAI #GraphLearning #GraphNeuralNetworks

GDGB: The first benchmark for generative dynamic text-attributed graph learning, offering a foundation for advancing research in DyTAG generation. #AI #GraphLearning
🔎#GNN case study: 73% Improvement predicting “next” best financial offers to customers at a leading online bank in 3-days of modeling across 3B records. Read more: hubs.ly/Q02bV6PR0 #PredictiveAI #GraphLearning #GraphNeuralNetworks

OpenFGL: A Comprehensive Benchmark for Advancing Federated Graph Learning itinai.com/openfgl-a-comp… #FederatedLearning #GraphLearning #AI #OpenFGL #DataPrivacy #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #machinelearning #technology #deeplearning @…

Excited to participate in the industry panel in the #stanford #GraphLearning workshop, sharing tho. Graph ML remains an exciting topic in many industrial segments, with new opportunities rising up thru #GenAI.

Looking forward to today's AI talk at CAIDAS at @Uni_WUE, where we are proud to feature @Pseudomanifold with his talk "Towards Topological Machine Learning: An Emerging Research Field". Join us at 16:15 in lecture hall 0.001 in the Z6 building! #AI #CAIDAS #GraphLearning

📢 New blog post alert! "Symmetric Graph Contrastive Learning against Noisy Views for Recommendation" introduces a model-agnostic SGCL method with theoretical guarantees to enhance recommendation systems. Check it out here: bit.ly/3WX14IU #recommendations #graphlearning
📢 New paper: Robustness Potential Explorer (RPE) A 3-part framework to visualize & predict network robustness. ✅ Outperforms CNN/GNN methods 🔍 RPE-F | RPE-V | RPE-P #AI #NetworkRobustness #GraphLearning (content generated by Copilot) ieeexplore.ieee.org/abstract/docum…
Join us for the Stanford Graph Learning Workshop 2025! 🗓️Oct 14, 2025 📍Stanford University 🧠Topics: Agents, RFMs & LLM Inference. Save your spot to explore the future of #AI, #LLMs and #GraphLearning with leading experts. Register now: snap.stanford.edu/graphlearning-…

🚀 Excited to share our paper got accepted at DiffCoAlg@NeurIPS 2025 @diffcoalg !🎉 🙏 Thanks to @shrutimoy, Binita Maity, Anant Kumar, @adagu & @cse_iitgn . #NeurIPS2025 #GNN #GraphLearning #AIResearch

At @cp_conf our coordinator Sylvie Thiébaux delivered an invited talk on Graph Learning for Planning, highlighting how graph-based methods can advance heuristic search in automated planning. #Planning #AI #Graphlearning #TUPLESAI 👉bit.ly/419Xahk

🚨 BREAKTHROUGH in Graph Learning! What if each node in your graph could plan, reason, and act like a mini-agent—powered by an LLM? 🤯 That’s exactly what ReaGAN does. And it might just outsmart classic GNNs. Let me explain 🧵👇 #AI #GraphLearning #LLM #MachineLearning

AIHybets builds a live, evolving Semantic Graph— Each node = a signal, insight, term, or prompt. You don’t interact with the graph. You become part of it. #SemanticAI #GraphLearning
✨ Check our latest paper: Graph World Model (GWM): Towards a Unified Foundation World Model for Structured and Unstructured Data 📄 Paper: arxiv.org/pdf/2507.10539 💻 Code: github.com/ulab-uiuc/GWM #AI #WorldModel #GraphLearning #FoundationModel #Multimodal #GWM
GDGB: The first benchmark for generative dynamic text-attributed graph learning, offering a foundation for advancing research in DyTAG generation. #AI #GraphLearning
#CallforPaper 💫 Advances in Graph Learning and Representation Models for Complex Network Analysis This SI aims to bring together leading-edge research that explores the design, implementation, and application of #GraphLearning and #RepresentationModel. mdpi.com/journal/BDCC/s…


NITheCS & CoRE AI Masterclass: 'An Introduction to Graph Learning & Signal Processing' 🎓 With Dr Fei He & Stephan Goerttler (Coventry University) 🗓️ Tue, 27 May 2025 🕚 11:00–13:00 SAST 🔗 buff.ly/6nfB5ui #GraphLearning #SignalProcessing #AI #CoREAI #MachineLearning

#234 Graph Learning Explained: How Machines Understand Complex Relationships #GraphLearning #MachineLearning #GraphNeuralNetworks #DataScience #ArtificialIntelligence #DeepLearning #GraphTheory #AI #DataScienceDemystifiedDailyDose linkedin.com/pulse/234-grap…
Graph learning has evolved significantly. Early work in graph analysis was all about uncovering hidden patterns and relationships. Discover the journey here: ift.tt/9Rdnfbq #GraphLearning #DataScience #Evolution #Analytics #byAI
Our next talk will be given by @lrjconan on "SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups". Join us on Apr 30 (Wed) at **5pm** (CET). Check out dsiseminar.github.io for details. #graphlearning #diffusionmodel

🙏 Huge thanks to my co-author @KishanGurumurty and advisor @sh_charu for the collaboration, guidance, and insights throughout this journey. #GraphLearning #FederatedLearning #NeuralODE #GNN #AIResearch #TMLR @iiit_hyderabad
Graph learning's evolution is unfolding! 🚀 Google Research shares key insights. What future breakthroughs will it unlock? 🤔 buff.ly/Cmf5rHz #graphlearning #airesearch #googleresearch #machinelearning
From PageRank to modern graph learning—how far have we come? @Google researchers trace the evolution of graph-based AI and its breakthroughs. What’s next for graph learning? 🤔🚀 🔗 Read more: [research.google/blog/the-evolu…] #GraphLearning #AI #MachineLearning
The evolution of graph learning: research.google/blog/the-evolu… @Google researchers describe how graphs and graph learning have evolved since the advent of PageRank in 1996, highlighting key studies and research.

🚀 Excited to share our paper got accepted at DiffCoAlg@NeurIPS 2025 @diffcoalg !🎉 🙏 Thanks to @shrutimoy, Binita Maity, Anant Kumar, @adagu & @cse_iitgn . #NeurIPS2025 #GNN #GraphLearning #AIResearch

At @cp_conf our coordinator Sylvie Thiébaux delivered an invited talk on Graph Learning for Planning, highlighting how graph-based methods can advance heuristic search in automated planning. #Planning #AI #Graphlearning #TUPLESAI 👉bit.ly/419Xahk

🚨 BREAKTHROUGH in Graph Learning! What if each node in your graph could plan, reason, and act like a mini-agent—powered by an LLM? 🤯 That’s exactly what ReaGAN does. And it might just outsmart classic GNNs. Let me explain 🧵👇 #AI #GraphLearning #LLM #MachineLearning

📊#GNN case study: 73% better predicting “next best" offers at an online bank in 3-days of modeling across 3B records. Learn more: hubs.ly/Q02dclSP0 #PredictiveAI #GraphLearning #GraphNeuralNetworks

Get news and updates from Kumo AI. We're bringing the most powerful #GraphLearning approaches, proven in research, to the enterprise. hubs.ly/Q02g3LXK0

@scne just presented their latest work at @ACMRecSys #GraphLearning session. This work, co-authored by @dmalitesta @alberto_mancino @walteranelli @TommasoDiNoia Explores the relationship between topological datasets characteristics and GNN based recommender systems.

🔎#GNN case study: 73% Improvement predicting “next” best financial offers to customers at a leading online bank in 3-days of modeling across 3B records. Read more: hubs.ly/Q02bV6PR0 #PredictiveAI #GraphLearning #GraphNeuralNetworks

OpenFGL: A Comprehensive Benchmark for Advancing Federated Graph Learning itinai.com/openfgl-a-comp… #FederatedLearning #GraphLearning #AI #OpenFGL #DataPrivacy #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #machinelearning #technology #deeplearning @…

🔎#GNN case study: 40% Improvement in merchant recommendations for on-demand delivery service in 4-days across 3B records. Read more: hubs.ly/Q02dcy4F0 #PredictiveAI #GraphLearning #GraphNeuralNetworks

Dive into #GraphLearning at the Stanford Graph Learning Workshop 2023! FREE online stream next Tuesday, Oct 24. Discover cutting-edge ML advancements & connect with industry leaders. Register now: hubs.ly/Q0268yvR0

Highlights of today's Preconference Tutorials for #iccins2023 #mylavaram Dr.Tushar Semwal and Mr.Nahar Singh delivered the talks on #graphlearning #generativeai The Tutorials Chair and the committee members felicitated the resource persons.




A really good webinar on how effective #GraphLearning can be for your customer growth initiatives: #LTV #Churn #CustomerRetention hubs.ly/Q01X21pB0

AnyGraph: An Effective and Efficient Graph Foundation Model Designed to Address the Multifaceted Challenges of Structure and Feature Heterogeneity Across Diverse Graph Datasets itinai.com/anygraph-an-ef… #GraphLearning #AnyGraph #AI #DataScience #MachineLearning #ai #news #llm #…

Our next talk will be given by @Pseudomanifold on "Vertex, Edge, Clique: What's in a Graph?". Join us on Nov 20 (Wed) at **2pm** (CET). Check out dsiseminar.github.io for details. #graphlearning

Excited to participate in the industry panel in the #stanford #GraphLearning workshop, sharing tho. Graph ML remains an exciting topic in many industrial segments, with new opportunities rising up thru #GenAI.

In this fresh survey paper, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks and applications. arxiv.org/abs/2212.05532 doi.org/10.1145/3570906 #graphlearning #AI #machinelearning #anomalydetection #artificialintelligence

Join us for the Stanford Graph Learning Workshop 2025! 🗓️Oct 14, 2025 📍Stanford University 🧠Topics: Agents, RFMs & LLM Inference. Save your spot to explore the future of #AI, #LLMs and #GraphLearning with leading experts. Register now: snap.stanford.edu/graphlearning-…

If your research is somehow related to graph learning, consider submitting a paper to IEEE TNNLS Special Issue on Graph Learning. See CFP: xia.ai/tnnls-si-gl #graphlearning #AI #machinelearning #deeplearning #networks #graphs #Brain

Deadline extended to 1 July 2023. Early submissions are encouraged/preferred. IEEE TNNLS Special Issue on Graph Learning. See CFP: xia.ai/tnnls-si-gl #GraphLearning #AI #machinelearning #datascience #deeplearning #networks #graphs

Sadly being unable to attend #TheWebConf2023 #WWW2023 in person. But we do have two full papers being published there, both on #graphlearning. Full text FREE ACCESS @ACMDL doi.org/10.1145/354350… doi.org/10.1145/354350…


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