#temporalnetworks
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schichmax.bsky.social
#TimeSeriesAnalysis for #TemporalNetworks

Based on the abstract and inspection of figures, the impact of this work will be huge!
wetuad.bsky.social Lucas Lacasa @wetuad.bsky.social · Dec 5
🚨 New preprint out!

We build **scalar** time series embeddings of temporal networks !

The key enabling insight : the relevant feature of each network snapshot... is just its distance to every other snapshot!

Work w/ FJ Marín, N. Masuda, L. Arola-Fernández

arxiv.org/abs/2412.02715
December 5, 2024 at 9:47 AM
alessianetwork.bsky.social
🚀 The TENET @ @netsciconf.bsky.social program is live!
Talks, posters & discussions all about temporal networks—don’t miss it!

🗓 Full program: sites.google.com/view/tenet-n...

#NetSci2025 #TENET #TemporalNetworks #ComplexSystems #NetworkScience
TENET@Netsci25
Temporal networks are powerful models for understanding dynamical systems where entities and interactions appear and vanish as time goes by. Unlike static networks, which simply provide a snapshot of ...
sites.google.com
March 25, 2025 at 3:07 PM
leifeld.bsky.social
New paper 🚨: "Fully Bayesian Estimation of Temporal Decay in Ordinal Relational Event Models" with Jackie Siaw Tze Wong in #CSDA: Computational Statistics and Data Analysis (doi.org/10.1016/j.cs...). #relevent #sna #networkscience #bayesian #rem #temporalnetworks #computational #statistics
May 29, 2026 at 1:50 PM
getnews-me.bsky.social
A supra‑Laplacian framework for clustering evolving networks, in the Julia package TemporalNetworks.jl, outperforms Leiden on US Senate voting data. https://getnews.me/spectral-clustering-for-evolving-networks-using-the-supra-laplacian/ #supralaplacian #temporalnetworks
Spectral Clustering for Evolving Networks Using the Supra‑Laplacian
September 22, 2025 at 7:52 PM
r-romero.bsky.social
📄 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
Multiresolution Analysis and Statistical Thresholding on Dynamic Networks
Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then comp...
arxiv.org
June 7, 2025 at 2:37 PM
r-romero.bsky.social
📄 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
Multiresolution Analysis and Statistical Thresholding on Dynamic Networks
Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then comp...
arxiv.org
June 7, 2025 at 2:30 PM
r-romero.bsky.social
📄 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
Multiresolution Analysis and Statistical Thresholding on Dynamic Networks
Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then comp...
arxiv.org
June 7, 2025 at 2:21 PM