Our new Local #CommunityDetection in #DynamicGraphs Using #PersonalizedCentrality
http://bit.ly/2vJEOCl
@Algorithms_MDPI @MDPIOpenAccess

Researchers introduced GN‑CDEs, a framework that learns node embeddings on evolving graphs and beats snapshot baselines accurately on temporal link prediction. Read more: https://getnews.me/graph-neural-cdes-enable-continuous-dynamic-graph-embeddings/ #gncde #dynamicgraphs

Graph‑Variate Neural Networks meld a graph backbone with on‑the‑fly connections, keeping linear complexity. They beat graph baselines and match LSTM/Transformer scores on benchmarks. Read more: https://getnews.me/graph-variate-neural-networks-advance-dynamic-signal-modeling/ #gvnn #dynamicgraphs

4/15 Comparison to Existing Frameworks: CubeCL aims to enable algorithm development in Rust with runtime integration into compilers for dynamic graph fusion. 🔥 This is its unique value! #DynamicGraphs #Compilers #RustGPU

📄 Preprint available:
 arxiv.org/abs/2506.01208

Joint work with @tijldebie.bsky.social @nickheard @alexandermodell
#TemporalNetworks #DynamicGraphs #NetworkScience #ChangeDetection #Cybersecurity #SignalProcessing #Wavelets #StatisticalLearning #TimeSeries #GraphML

📄 Full preprint available:
arxiv.org/abs/2506.01208

Joint work with @tijldebie.bsky.social @alexandermodell @nickheard
#TemporalNetworks #DynamicGraphs #NetworkScience #ChangeDetection #Cybersecurity #SignalProcessing #Wavelets #StatisticalLearning #TimeSeries #GraphML

📄 Link to the full preprint:
arxiv.org/abs/2506.01208

Joint work with @tijldebie.bsky.social @alexandermodell @nickheard
#TemporalNetworks #DynamicGraphs #NetworkScience #ChangeDetection #Cybersecurity #SignalProcessing #Wavelets #StatisticalLearning #TimeSeries #GraphML

For a comprehensive understanding, you can access the full paper here: [Valid Conformal Prediction for Dynamic GNNs](arxiv.org/pdf/2405.1...)

#GraphNeuralNetworks #ConformalPrediction #DynamicGraphs #MachineLearning #UncertaintyQuantification