Mobility and spatial networks 1 (Chair: LUCA MARIA AIELLO)
Topological Trajectory Classification and Landmark Inference on Simplicial Complexes
Presenter: Vincent Grande
Time: Wed 14:30 - 14:45
Authors: Vincent Grande (RWTH Aachen University)*; Josef Hoppe (RWTH Aachen University); Florian Frantzen (RWTH Aachen University); Michael Schaub (RWTH Aachen University)
Abstract
We consider the problem of classifying trajectories on a discrete or discretised 2-dimensional manifold modelled by a simplicial complex.Previous works have proposed to project the trajectories into the harmonic eigenspace of the Hodge~Laplacian, and then cluster the resulting embeddings. However, if the considered space has vanishing homology (i.e., no "holes"), then the harmonic space of the 1-Hodge Laplacian is trivial and thus the approach fails. Here we propose to view this issue akin to a sensor placement problem and present an algorithm that aims to learn "optimal holes" to distinguish a set of given trajectory classes. Specifically, given a set of labelled trajectories, which we interpret as edge-flows on the underlying simplicial complex, we search for 2-simplices whose deletion results in an optimal separation of the trajectory labels according to the corresponding spectral embedding of the trajectories into the harmonic space.Finally, we generalise this approach to the unsupervised setting.
Behavior-based contact structure in mobility networks
Presenter: Esteban Moro
Time: Wed 14:45 - 15:00
Authors: Hamish Gibbs (Northeastern University)*; Esteban Moro (Northeastern University)
Abstract
Contact networks, which describe individuals' physical interactions, have broad applications for understanding diffusion processes in social networks, from the spread of information to the transmission of infectious disease. Commonly, contact networks are segmented by individuals' demographic characteristics, showing the contact probabilities (or "mixing") between different demographic sub-populations. While demographic-stratification of contact networks can reveal important structure in the patterns of interaction within a population, demographic features do not fully capture behavioral differences between individuals. In this project, we build on existing research identifying latent behavioral patterns in individual activity, which looks below the level of individual demographics to identify common patterns of activity across individuals' lifestyles. Using detailed GPS mobility data describing individuals' patterns of visitation to points of interest, we use Non-negative Matrix Factorization (NMF), a machine learning dimensionality reduction technique, to identify the key behavioral components which describe individuals' mobility activity. This is analogous to identifying the fundamental "genes" which, when taken together, form an individual's unique lifestyle. After decomposing individual-level mobility data into behavioral components, we compute contact networks between individuals based on their proximity in space and time. We then compare observed patterns of interaction with respect to the distribution of individuals' latent behavioral features. Our approach can highlight behavioral homophily in networks, were contacts occur more frequently between individuals with similar behavioral characteristics, and behavior-based community structure which is not captured by demographic features.
Minimum-cost percolation on US air transportation network
Presenter: Minsuk Kim
Time: Wed 15:00 - 15:15
Authors: Minsuk Kim (Indiana University)*; Christopher Diggans (Air Force Research Laboratory’s Information Directorate); Filippo Radicchi (Indiana University)
Abstract
Transportation infrastructure systems are the backbone of our society, fostering economic growth and driving innovation by conveying people and goods from origin to destination in a timely manner. Successful operation of these infrastructures depends on efficient routing of origin-destination demands given a fixed amount of resources available. Here, we extend the previous framework of the shortest-path percolation, which is a stylized model defined on a simple graph describing how the macroscopic structure of the transportation system collapses as resources are consumed to fulfill the origin-destination demands, considering a generalized structure of the transportation network with time-stamp, directionality, weight, and capacity: the minimum-cost percolation (MCP) model. We applied the MCP framework to the US air transportation system using publicly available data and conducted extensive experiments considering various costs and different origin-destination demands. Our results indicate that cooperation among airline carriers through shared flights can significantly improve the overall efficiency of the US air transportation system. These findings demonstrate the potential of the MCP framework to guide cooperative policies that enhance resource utilization and overall efficiency.
Behavioral response to mobile phone evacuation alerts
Presenter: Erick Elejalde Sierra
Time: Wed 15:15 - 15:30
Authors: Erick Elejalde (L3S Research Center)*; Timur Naushirvanov (Department of Network and Data Science, Central European University); Kyriaki Kalimeri (ISI Foundation); Elisa Omodei (Department of Network and Data Science, Central European University); Márton Karsai (Department of Network and Data Science, Central European University); Loreto Bravo (IDS, Universidad del Desarrollo); Leo Ferres ( IDS, Universidad del Desarrollo)
Abstract
SMS-based alerts during emergencies are a highly effective communication tool due to their broad reach, low cost, and immediacy [1]. However, overly broad alerts can lead to over-evacuation, straining infrastructure, and eroding public trust. We analyzed the SMS-based alerts issued during Chile’s most devastating wildfire in 30 years, which struck Valpara\'so on February 2, 2024, causing 137 fatalities and affecting over 16,000 people. Using spatio-temporal data from a national mobile network and census-based proxies for socioeconomic status (SES), we categorized telecommunication towers into lower, middle, and higher SES groups. Official alerts from SENAPRED provided the timing and location of evacuation messages, while to analyse people's mobility, we employed XDR spatio-temporal data from a national mobile communication company.Our findings show that the first SMS alert prompted significant evacuation behavior across all SES groups, regardless of proximity to the wildfire (Figure 1a). Higher SES areas exhibited fewer connections (Figure 1b), suggesting greater capacity for proactive evacuation. Interrupted time-series analysis confirmed that the initial alert was the primary driver of evacuation, with higher SES evacuations influenced more by prudence than direct alert targeting (Figure 1c). Our findings show several interesting patterns; initial alerts elicit stronger responses, repeated alerts risk desensitization, and SES shapes evacuation patterns, with higher SES groups better positioned to act. These insights inform strategies to enhance emergency communication, ensuring precision and improved safety for all communities.
Mobility and cohesion of personal networks over the lifecourse of an entire population
Presenter: Eszter Bokanyi
Time: Wed 15:30 - 15:45
Authors: Eszter Bokanyi (UvA)*; Yuliia Kazmina (UvA); Frank Takes (Leiden University); Eelke Heemskerk (UvA)
Abstract
Changing technology fosters higher levels of human mobility creating both new connections between faraway places and rearranging the spatial structure of already existing connections. Both mechanisms and their impact on network topology are little understood in the literature.In this work, we address this gap by leveraging a unique longitudinal population-scale network dataset sourced from Statistics Netherlands. This network contains family, work, school, household, and next-door neighbor connections derived from administrative registers, that together constitute a multilayer social opportunity structure for all residents of the Netherlands between 2009 and 2022. We follow the patterns of individuals’ ego networks over time, and measure their size, closure, and geographical dispersion.First, we show that while the average size of ego networks stays stable over the observed period, average closure drops by as much as 10%, leading to more open local network structures. Second, we see that the average geographical distance from network neighbors grows, and in parallel, the average share of network neighbors in the same municipality decreases. Third, we link the two using multivariate difference-in-difference type regressions which show that the observed decrease in the closure is indeed significantly linked to the growing geographic dispersion. The regressions thus confirm that people’s mobility is linked to more open ego networks, which potentially impacts on people’s access to information, or the level of trust in communities.This work is among the first ones that aims to map the temporal network of an entire population structure comprehensively [2]. As such, it offers a starting point for a wide variety of impactful network science research at the level of a complete population.
Dynamic models of gentrification
Presenter: Giovanni Mauro
Time: Wed 15:45 - 16:00
Authors: Giovanni Mauro (Scuola Normale Superiore); Nicola Pedreschi (University of Oxford)*; Renaud Lambiotte (University of Oxford); Luca Pappalardo (ISTI-CNR)
Abstract
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.