#graphlearning نتائج البحث
🚀 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-…
📢 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.
Get news and updates from Kumo AI. We're bringing the most powerful #GraphLearning approaches, proven in research, to the enterprise. hubs.ly/Q02g3LXK0
🚨 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
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.
A really good webinar on how effective #GraphLearning can be for your customer growth initiatives: #LTV #Churn #CustomerRetention hubs.ly/Q01X21pB0
GDGB: The first benchmark for generative dynamic text-attributed graph learning, offering a foundation for advancing research in DyTAG generation. #AI #GraphLearning
Lastly, if you're interested in AI for science and graph-based learning in scientific applications, follow for more updates! #AIforScience #GraphLearning
In short: We introduced several #graphdatabases (e.g., Neo4j) and #graphLearning algorithms (e.g., Graph Neural Networks) and analyzed their advantages and disadvantages.
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
Graphs power real-world solutions in #ML—from traffic prediction to molecular insights. 🚗📷 Discover Google’s role in graph-based ML. #GraphLearning
Graphs provide a powerful way to model & solve many real-life problems, from traffic prediction to understanding why molecules smell. Learn more about the recent history of graph-based #ML & the role that Google researchers have played in the field →goo.gle/42aABbR
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
📊#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
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
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
🔎#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
📢 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
![taofeng_uiuc's tweet card. [ICML 2025]"Graph World Model", Tao Feng, Yexin Wu, Guanyu Lin, Jiaxuan You - ulab-uiuc/GWM](https://pbs.twimg.com/card_img/1983749088181882880/XSgJUobB?format=jpg&name=orig)
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
🚀 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-…
Happy new year! Our first talk of 2025 will be given by @yanning_shen (UC Irvine) on "Demystifying and Mitigating Unfairness for Learning over Graphs". Join us on Jan 22 (Wed) at *4pm* (CET). Check out dsiseminar.github.io for details. #graphlearning
📊#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
🔎#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
🔎#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
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
🚨 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
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
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 @…
@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.
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
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.
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…
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
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 #…
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