#graphlearning search results
probably the best website to get into geometric deep learning and graph neural networks contains a course, lecture, blogs, random keynotes.
Currently reading the 2nd edition of "Graph Machine Learning" by Aldo Marzullo, Enrico Deusebio, and Claudio Stamile. Published by @PacktPublishing
🚀 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
Day 77/90 #90DayChallenge DSA: Today, I deep-dived into Graph Data Structure — explored how it helps in representing relationships between entities. ✨Learned about: Why we need graphs How to implement graphs Adjacency Matrix & Adjacency List Different types of graphs #DSA
Graph Algorithms for #DataScience: amzn.to/48uHOZc ➕ Python Code samples: memgraph.com/blog/graph-alg…
If you’re into graph theory, machine learning, or representation learning, this is a great resource to stay ahead of how graphs and LLMs are converging in real-world applications. Get you copy today: packt.link/qUPZx
What is supervised learning? Rather than explaining it by words, let's see an explanatory graphic example. Seems easier now, doesn't it? Data > @simplilearn °°° #Infographic by @LindaGrass0 & @antgrasso #MachineLearning #ArtificialIntelligence #Tech
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-…
Graph theory will seriously enhance your engineering skills. Here's why you must be familiar with graphs:
the graph consists of the actual graphical information and the program which can manipulate the graph
Study Graph Theory it’s how the universe connects its dots. Every living and digital system neurons, the web, markets, galaxies follows its logic. It’s not about numbers; it’s about relationships. It teaches you that structure creates intelligence, not chaos. Every idea, friend,…
study graph theory the entire universe runs on connections. neurons, roads, networks, friendships, computers, logistics, even ideas; all are graphs. graph theory is the math of relationships. it teaches you how things interact, not just what they are. you’ll start seeing everyt
Introduction to Graph #NeuralNetworks — A Starting Point for #MachineLearning Engineers: arxiv.org/abs/2412.19419 [49-page PDF] #AI #ML #DeepLearning #DataScience #DataScientist
![KirkDBorne's tweet image. Introduction to Graph #NeuralNetworks — A Starting Point for #MachineLearning Engineers: arxiv.org/abs/2412.19419 [49-page PDF]
#AI #ML #DeepLearning #DataScience #DataScientist](https://pbs.twimg.com/media/G3rbuQoWwAArZ2E.jpg)
Graphons are mathematical objects that model the structure of massive networks. In machine learning, they provide a powerful framework for analyzing and generating large graphs. They are used to estimate the underlying structure of a network, predict missing links, and understand…
The Graph ensures onchain data remains available and queryable via multiple indexers. Reliable data, even under stress or downtime.
Tired of blindly tweaking layout parameters to visualize your graph? Our #MachineLearning model builds a WYSIWYG interface for you to intuitively produce a layout you want! Check out our #IEEEVIS paper (arxiv.org/abs/1904.12225) and demo (kwonoh.net/dgl). #NetworkScience
New preprint on Graph Transformers! We show that a vanilla Transformer on nodes and edges becomes a powerful graph learner (in both theory and practice) if simple auxiliary input embeddings are provided. We call this Tokenized Graph Transformer (TokenGT). arxiv.org/abs/2207.02505
Working with graph-structured data? Check out our recent survey for Machine Learning on Graphs: arxiv.org/pdf/2005.03675… We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.
📢 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/1978616240961957888/Hk9Fmq6Q?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…
linkedin.com
#234 Graph Learning Explained: How Machines Understand Complex Relationships
Data Science Demystified Daily Dose Introduction In a world where everything is interconnected—from your social media interactions to your daily commute—graph learning helps machines understand those...
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
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
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-…
📊#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.
🚨 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% 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
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 #…
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.
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.
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
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
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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