Search results for #GraphNeuralNetworks
We validated HallmarkGraph on over 10,000 tumor samples across 26 cancers and 405 subtypes up to eight levels. Our model is effective and scalable, paving the way for more robust cancer classification. #DataScience #GraphNeuralNetworks #Oncology #Genomics
🔥 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
Self-Supervised Spatiotemporal Masking Strategy-Based Models for #TrafficFlow Forecasting ✏️ Gang Liu et al. 🔗 brnw.ch/21wVbPd Viewed: 2242; Cited: 5 #mdpisymmetry #selfsupervisedlearning #graphneuralnetworks @SCUT1918
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
Graph Neural Networks and the Shape of Thought by @b_k_hela at #ITNEXT. #neuroscience #artificialintelligence #cognitivescience #deeplearning #graphneuralnetworks itnext.io/graph-neural-n… (f)
Graph Neural Networks and the Shape of Thought by @b_k_hela at #ITNEXT. #neuroscience #artificialintelligence #cognitivescience #deeplearning #graphneuralnetworks itnext.io/graph-neural-n… (t)
Ollivier–Ricci Curvature Based #SpatioTemporal #GraphNeuralNetworks for #TrafficFlow Forecasting ✏️ Xing Han et al. 🔗 brnw.ch/21wUyub Viewed: 3172; Cited: 8 #mdpisymmetry #trafficforecasting
Graph Neural Networks and the Shape of Thought by @b_k_hela at #ITNEXT. #neuroscience #artificialintelligence #cognitivescience #deeplearning #graphneuralnetworks itnext.io/graph-neural-n… (s)
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…
Graph Neural Networks and the Shape of Thought by @b_k_hela at #ITNEXT. #neuroscience #artificialintelligence #cognitivescience #deeplearning #graphneuralnetworks itnext.io/graph-neural-n…
🔔 New Published Papers of #MDPIfutureinternet Title: A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network mdpi.com/1999-5903/17/7… #microservices #staticcodeanalysis #AI #machinelearning #graphneuralnetworks
📝 New article: "HeSQLNet: A Heterogeneous Graph Neural Network for SQL-to-Text Generation" by Junsan Zhang, Ao Lu, Junxiao Han, Yang Zhu, Yudie Yan, Juncai Guo, Yao Wan 👉 authors.elsevier.com/a/1lMjI3O8rCo9… #SQLtoText #GraphNeuralNetworks #HeterogeneousGraphs #AST #NLP #DeepLearning
Toady #GNNs are driving some of the most sophisticated AI innovations today. Yet, despite their potential across domains, GNNs still face critical challenges. Read more: bit.ly/43PJavo #GraphNeuralNetworks #GNNapplications #GCNnet #GAT #explainableAI #ML #ARTiBA
💥It's tutorial time for #GraphNeuralNetworks! Here's a part 1 step-by-step towards understanding how GNNs work. Sometimes, a refresh is exactly what you need. #GNNs #AI 🧵
... more efficient on modern hardware than sparse message passing. This leads to the perspective that Transformers are GNNs currently winning the hardware lottery". Transformers are #GraphNeuralNetworks. Chaitanya K Joshi. 27 Jun 2025. arxiv.org/abs/2506.22084