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Postdoctoral Position in Machine Learning for Organic Synthesis 📍 Rouen, France Apply now: researchhires.com/position/75128… #machinelearning #GraphNeuralNetworks #Postdocs #ResearchHires #OrganicSynthesis

New article! Using graph neural networks and frequency domain data for automated operational modal analysis of populations of structures 👉 bit.ly/4mFGu9e By Xudong Jian, Yutong Xia, @gduthe_, Kiran Bacsa, Wei Liu and @Lne_Chatzi #DeepLearning #GraphNeuralNetworks

🏢 One of our most-cited papers in Dynamics! “Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method.” GCNs + LSTMs boost accuracy in predicting building energy use. Read: mdpi.com/2673-8716/4/2/… #BuildingEnergy #GraphNeuralNetworks

🧠 New method for robust graph inference in EEG networks. ✅ Combines multiple connectivity estimates into a stable brain graph 🔍 Tested on motor imagery & Alzheimer’s data → more reliable biomarkers 🔮 Future → real-time BCI & clinical monitoring #EEG #GraphNeuralNetworks

🎙️ AI Frontiers Sep 7, 2025: 19 Papers on Efficient, Explainable & Trustworthy ML #MachineLearning #AIResearch #GraphNeuralNetworks Watch full episode: youtube.com/watch?v=rO7c8b…

New algorithms enable efficient machine learning with symmetric data bit.ly/4nowuSz #MachineLearning #SymmetryInAI #GraphNeuralNetworks #DataScience #AIResearch #EfficientAlgorithms #DeepLearning #ScientificComputing #Smartsystems

"Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning," by A. Said, Y. Wei, O.U. Ahmad, M. Shabbir, W. Abbas, X. Koutsoukos Date: 15 August 2025 Link: ieeexplore.ieee.org/document/11126… #graphneuralnetworks #socialnetworking #controltheory #ojcsys

🔥 Read our Highly Cited Paper 📚XGBoost-Enhanced #GraphNeuralNetworks: A New Architecture for Heterogeneous Tabular Dat 🔗mdpi.com/2076-3417/14/1… 👨‍🔬by Liuxi Yan and Yaoqun Xu 🏫Harbin University of Commerce #nodeprediction #nodeclassification

Insider trading rarely looks like a Hollywood heist. More often, it’s whispered tips over dinner, quiet trades through family accounts. #QuantFinance #AI #GraphNeuralNetworks #InsiderTrading open.substack.com/pub/llmquant/p…

Graph RAG vs RAG: Which One Is Truly Smarter for AI Retrieval? | Data Science Dojo #GraphNeuralNetworks, #NLP, #DataScience, #MachineLearning, #ArtificialIntelligence datasciencedojo.com/blog/graph-rag…

ReaGAN: Transforming Graph Nodes into Autonomous Agents for Enhanced AI Decision-Making #ReaGAN #GraphNeuralNetworks #ArtificialIntelligence #MachineLearning #AIResearch itinai.com/reagan-transfo… Understanding ReaGAN: A Revolutionary Approach to Graph Neural Networks The introd…

Check this newly published article "Defending #GraphNeuralNetworks Against #BackdoorAttacks via Symmetry-Aware Graph Self-Distillation" at brnw.ch/21wUVQU Authors: Hanlin Wang, Liang Wan and Xiao Yang #mdpisymmetry #artificialintelligencesecurity

Quantum Graph Neural Networks Improve Quantum Simulations Read more on quantumcomputer.blog/quantum-graph-… #QuantumGraphNeuralNetworks #GraphNeuralNetworks #transversefieldIsingmodel #DensityMatrixRenormalizationGroup #nearestneighbor #QuantumGNN #news #technews #technology #technologynews…

Ollivier–Ricci Curvature Based #SpatioTemporal #GraphNeuralNetworks for #TrafficFlow Forecasting ✏️ Xing Han et al. 🔗 brnw.ch/21wUyub Viewed: 3172; Cited: 8 #mdpisymmetry #trafficforecasting

Read #Article "Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks". See more details at: mdpi.com/2078-2489/15/2… #knowledgegraphs #Graphneuralnetworks @ComSciMath_Mdpi

What if predicting a stock’s movement wasn’t just about its own data, but about its relationships? #AIinFinance #QuantResearch #GraphNeuralNetworks #StockPrediction #DeepLearning open.substack.com/pub/llmquant/p…

Following the "Tutorial on #UserProfiling with #GraphNeuralNetworks and Related Beyond-Accuracy Perspectives" at #UMAP2023 by @erasmopurif11, @ludovicoboratto and @ernestowdeluca 🚀

Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks

"Non-convolutional Graph Neural Networks" by @YuanqingWang , and @kchonyc Paper: arxiv.org/abs/2408.00165 #graphneuralnetworks

Graph-powered machine learning mind map #MachineLearning #graphneuralnetworks #deeplearning

Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks

"GraphAny: A Foundation Model for Node Classification on Any Graph" by @AndyJiananZhao, @michael_galkin, @mmbronstein, @zhu_zhaocheng, @tangjianpku et al. Paper: arxiv.org/abs/2405.20445 #graphneuralnetworks #foundationmodels

Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks

Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks

How does over-squashing affect the power of GNNs? Di Giovanni et al.: arxiv.org/abs/2306.03589 #GraphNeuralNetworks #GNN #DeepLearning

"Homomorphism Counts as Structural Encodings for Graph Learning" by @mmbronstein, @ismaililkanc, @mat_lanzinger et al. #MachineLearning #graphneuralnetworks

How does over-squashing affect the power of GNNs? Di Giovanni et al.: arxiv.org/abs/2306.03589 #GraphNeuralNetworks #GNN #DeepLearning

How does over-squashing affect the power of GNNs? Di Giovanni et al.: arxiv.org/abs/2306.03589 #GraphNeuralNetworks #GNN #DeepLearning

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Jin et al.: arxiv.org/abs/2307.03759 #DeepLearning #GraphNeuralNetworks #TimeSeries

Benchmarking Graph Neural Networks Dwivedi et al.: arxiv.org/abs/2003.00982 #ArtificialIntelligence #DeepLearning #GraphNeuralNetworks

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Jin et al.: arxiv.org/abs/2307.03759 #DeepLearning #GraphNeuralNetworks #TimeSeries

"Future Directions in Foundations of Graph Machine Learning" by @HaggaiMaron ,@ismaililkanc, @ffabffrasca, @dereklim_lzh, @mmbronstein et al. Paper: arxiv.org/abs/2402.02287 #graphneuralnetworks

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Jin et al.: arxiv.org/abs/2307.03759 #DeepLearning #GraphNeuralNetworks #TimeSeries

"Link Prediction with Relational Hypergraphs" by @hxyscott, @mmbronstein , @ismaililkanc et al. Paper: arxiv.org/abs/2402.04062 #graphneuralnetworks

I will be presenting my poster at #EUROTOX2024, in Copenhagen, in a few days! Very excited. I have been using #AI, via #GraphNeuralNetworks and #KnowledgeGraphs, to build toxicological #QSAR models for brominated flame retardants. You can find my abstract published in…

It has been a long time since I did an online course 👨‍🎓 . I was focused on my work this year with #GraphNeuralNetworks as my niche and never got the time or the mental fortitude to catch up to the intimidating and overwhelming amount of progress being made in the #LLM and #genai…