Raphaël Romero

That’s Vernazza in Italy

- There is a service from the city called FietsAmbassade where you can rent cargo bikes if needed. There are also apps for that. The furniture shop Weba even rents cargo bike for a cheap price if you need to carry something heavy. Else people usually have large bike bags on their rear bike racks

- Trams and buses are abundent and made easy to use for people with reduced mobility. Of course this is not always perfect.

- There are fairly cheap and easy to use car-sharing services like Cambio/Dégage/Getaround in case people exceptionally need a car to carry heavy items.

- Motorized delivery vehicles are still allowed in the city center but they have to follow convoluted routes to get around the city. That's why many companies use cargo bikes instead, since they can circulate more directly from point A to point B and are more convenient to park

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...

📄 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

📊 Change detection is formulated as a statistical testing problem, classifying multiscale projections as either signal or noise over time.

🧠 In our last paper, we take a first step toward adaptive-resolution methods. Drawing on functional data analysis and statistical signal processing, we propose a wavelet decomposition framework that analyzes network dynamics across multiple scales.

🕵️‍♂️ In domains like cybersecurity, however, this fixed-resolution approach can be limiting — the appropriate time scale is often unknown and can vary over time.

🆕 New Work on Arxiv!

📡 Raw dynamic network data consists of timestamped interaction events between entities (nodes).

✂ Existing work on change detection or community detection often applies static network methods by aggregating interactions into fixed time windows.

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...

📄 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

📊 Change detection is formulated as a statistical testing problem, classifying multiscale projections as either
signal or noise over time.

🧠 In this paper, we take a first step toward adaptive-resolution methods. Drawing on functional data analysis and statistical signal processing, we propose a wavelet decomposition framework that analyzes network dynamics across multiple scales.

🕵️‍♂️ In domains like cybersecurity, however, this fixed-resolution approach can be limiting — the appropriate time scale is often unknown and can vary over time.

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...

📄 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...

📊 Change detection is formulated as a statistical testing problem, classifying multiscale projections as either signal or noise over time.

🧠 In our last paper, we take a first step toward adaptive-resolution methods. Drawing on functional data analysis and statistical signal processing, we propose a wavelet decomposition framework that analyzes network dynamics across multiple scales.

🕵️‍♂️ In domains like cybersecurity, however, this *fixed-resolution* approach can be limiting — the appropriate time scale is often unknown and can vary over time.

Thank you for the great event and for the Oscar ! 😃

Congratulations to the #NetSci2025 TENET award winners! They got Oscar trophies 😀