arXiv subjects

Minsuk Kim

Publications and source records attributed to Minsuk Kim.

12 recordsLinked to original sources

arXiv2026

Criticality and universality in network dismantling

Identifying the smallest set of elements whose removal dismantle a complex network, known as the network dismantling problem, is a fundamental task with many practical applications. Whereas network dismantling has been extensively studied over the past decade, most work has focused on developing efficient algorithms for large but finite networks. By contrast, the physics of the network dismantling process, namely how the network structural connectivity is affected by the removal of nodes or edges, remains largely unexplored in the thermodynamic limit. Here, we shed light on this understudied aspect of network dismantling by introducing an adaptive biased percolation process able to optimally dismantle a network. Through a systematic analysis of synthetic network models, we find that the proposed percolation process displays a universal phase transition, characterized by the abrupt and simultaneous disappearance of both the giant connected component and the largest 2-core, across networks with markedly different degree distributions. Simulations on real networks further support this universality, indicating that the physics of network dismantling is insensitive to a broad range of topological properties. Together, these results suggest that a topology-agnostic theory could be developed to explain the critical behavior of network dismantling.

physics.soc-ph↗

arXiv2026

Test-Beam Performance of the AstroPix Silicon Sensor for Imaging Calorimetry

AstroPix is a high-voltage CMOS HVCMOS monolithic active pixel sensor MAPS developed for future space-based gamma-ray missions. It is also a candidate technology for the imaging layer of the Barrel Imaging Calorimeter BIC in the ePIC experiment at the future Electron-Ion Collider EIC. We report the first AstroPix test-beam results obtained at the KEK Photon Factory Advanced Ring PF-AR and the CERN Proton Synchrotron PS T10 beam line in 2025, using the third prototype AstroPix-v3. AstroPix-v3 sensors were operated as both standalone tracking layers and imaging layers interleaved with prototype lead/scintillating-fiber Pb/SciFi calorimeter modules, using electron and hadron beams in the few-GeV/c momentum range. Event synchronization between the continuous readout of AstroPix-v3 and the trigger-based readout of the Pb/SciFi calorimeter was achieved using a common timestamp. The AstroPix-v3 sensors exhibit stable performance, reaching a maximum hit efficiency of 68 percent at a bias voltage of -400 V under pion-dominated beam conditions. When combined with the Pb/SciFi calorimeter, the AstroPix layers successfully capture the development of electromagnetic showers. Using Cherenkov-based particle identification, electron-induced events exhibit significantly higher hit multiplicities and broader spatial distributions than pion-induced events, thereby providing clear discrimination between electromagnetic and hadronic showers. These results demonstrate that AstroPix-v3 provides effective, high-granularity imaging of shower development and is well suited as an imaging layer in future calorimeter systems for both collider and space-based experiments.

physics.ins-det↗

arXiv2026

Beam test of a Pb/SciFi prototype for the Barrel Imaging Calorimeter at the Electron-Ion Collider

A Lead-Scintillating Fiber (Pb/SciFi) prototype for the Barrel Imaging Calorimeter (BIC) at the Electron--Ion Collider (EIC) was tested with electron beams at the CERN PS T10 beam line in August 2024. The prototype consisted of unit modules with a sampling structure of lead sheets and scintillating fibers, corresponding to a total depth of approximately $10.9\,X_{0}$. Beam tests were performed with electron momenta between 0.5 and 3~GeV/$c$ to evaluate the energy and timing performance of the prototype. This study characterizes the performance of a Pb/SciFi prototype and provides input for future beam tests, calibration and readout optimization, and the development of larger-scale prototypes.

physics.ins-det↗

arXiv2026

Shortest-path percolation on scale-free networks

The shortest-path percolation (SPP) model aims at describing the consumption and eventual exhaustion of a network's resources. Starting from a network containing a macroscopic connected component, random pairs of nodes are sequentially selected, and if the length of the shortest path connecting the node pairs is smaller than a tunable budget parameter, then all edges along such a path are removed from the network. As edges are progressively removed, the network eventually breaks into multiple microscopic components, undergoing a percolation-like transition. It is known that SPP transition on Erdős-Rényi networks (ERNs) belongs to same universality class as of the ordinary bond percolation if the budget parameter is finite; for unbounded budget, instead, the SPP transition becomes more abrupt than the ordinary percolation transition. By means of large-scale numerical simulations and finite-size scaling analysis, here we study the SPP transition on random scale-free networks (SFNs) characterized by power-law degree distributions. We find, in contrast with ordinary percolation, that the transition is identical to the one observed on ERNs, denoting independence from the degree exponent. Still, we distinguish finite- and infinite-budget SPP universality classes. Our findings follow from the fact that the SPP process drastically homogenizes the heterogeneous structure of SFNs before the SPP transition takes place.

physics.soc-ph↗

arXiv2025

Beyond Distance: Mobility Neural Embeddings Reveal Visible and Invisible Barriers in Urban Space

Human mobility in cities is shaped not only by visible structures such as highways, rivers, and parks but also by invisible barriers rooted in socioeconomic segregation, uneven access to amenities, and administrative divisions. Yet identifying and quantifying these barriers at scale and their relative importance on people's movements remains a major challenge. Neural embedding models, originally developed for language, offer a powerful way to capture the complexity of human mobility from large-scale data. Here, we apply this approach to 25.4 million observed trajectories across 11 major U.S. cities, learning mobility embeddings that reveal how people move through urban space. These mobility embeddings define a functional distance between places, one that reflects behavioral rather than physical proximity, and allow us to detect barriers between neighborhoods that are geographically close but behaviorally disconnected. We find that the strongest predictors of these barriers are differences in access to amenities, administrative borders, and residential segregation by income and race. These invisible borders are concentrated in urban cores and persist across cities, spatial scales, and time periods. Physical infrastructure, such as highways and parks, plays a secondary but still significant role, especially at short distances. We also find that individuals who cross barriers tend to do so outside of traditional commuting hours and are more likely to live in areas with greater racial diversity, and higher transit use or income. Together, these findings reveal how spatial, social, and behavioral forces structure urban accessibility and provide a scalable framework to detect and monitor barriers in cities, with applications in planning, policy evaluation, and equity analysis.

cs.CY↗

arXiv2025

Modeling resource consumption in the US air transportation system via minimum-cost percolation

We introduce a dynamic percolation model aimed at describing the consumption, and eventual exhaustion, of resources in transportation networks. In the model, rational agents progressively consume the edges of a network along demanded minimum-cost paths. As a result, the network undergoes a transition between a percolating phase where it can properly serve demand to a non-percolating phase where demand can no longer be supplied. We apply the model to a weighted, directed, temporal, multi-layer network representation of the air transportation system that can be generated using real schedules of commercial flights operated by US carriers. We study how cooperation among different carriers could improve the ability of the overall air transportation system in serving the demand of passengers, finding that unrestricted cooperation could lead to a 30% efficiency increase compared to the non-cooperative scenario. Cooperation would require major airlines to share a significant portion of their market, but it would allow also for an increased robustness of the system against perturbations causing flight cancellations. Our findings underscore some key benefits that could emerge by simply promoting code-share arrangements among US airlines without altering their current cost of operation.

physics.soc-ph↗

arXiv2024

Shortest-path percolation on random networks

We propose a bond-percolation model intended to describe the consumption, and eventual exhaustion, of resources in transport networks. Edges forming minimum-length paths connecting demanded origin-destination nodes are removed if below a certain budget. As pairs of nodes are demanded and edges are removed, the macroscopic connected component of the graph disappears, i.e., the graph undergoes a percolation transition. Here, we study such a shortest-path-percolation transition in homogeneous random graphs where pairs of demanded origin-destination nodes are randomly generated, and fully characterize it by means of finite-size scaling analysis. If budget is finite, the transition is identical to the one of ordinary percolation, where a single giant cluster shrinks as edges are removed from the graph; for infinite budget, the transition becomes more abrupt than the one of ordinary percolation, being characterized by the sudden fragmentation of the giant connected component into a multitude of clusters of similar size.

physics.soc-ph↗

arXiv2024

Reconstruction of multiplex networks via graph embeddings

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper of network science. Here, we develop a machine learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.

physics.soc-ph↗

arXiv2023

BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching

Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements.

q-fin.ST↗

arXiv2023

A Multi-Platform Collection of Social Media Posts about the 2022 U.S. Midterm Elections

Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.

cs.SI↗

arXiv2022

A simple and efficient model for epidemic control on multiplex networks

When an unprecedented infectious disease with high mortality and transmissibility emerges, immediate usage of vaccines or medicines is hardly available. Thus, many health authorities rely on non-pharmaceutical interventions through traceable fixed contacts. However, in reality, there is an additional type of transmission routes to the regular and fixed contacts: the random anonymous infection cases where non-pharmaceutical interventions are hardly feasible. In our study, such realistic situations are implemented by the susceptible-infected-recovered model with isolation on multiplex networks. The multiplex networks are composed of a fixed interaction layer and a layer with time-varying random interactions to represent the different types of disease spreading routes. The multiplex networks represent the combinations of the quenched disorder and annealed disorder. Here, we suggest a preemptive isolation protocol which isolates the second nearest neighbors of the hospitalized individuals and compare it with one of the most popular protocol adopted by many health organizations over the globe. From numerical simulations we find that our preemptive measure significantly reduces both the final epidemic size and the number of the isolated per unit time. Our finding suggests a better non-pharmaceutical intervention which can be adopted to various types of diseases even though the contact tracing is only partially available.

physics.soc-ph↗

arXiv2009

Area Problems Involving Kasner Polygons

Sequences of polygons generated by performing iterative processes on an initial polygon have been studied extensively. One of the most popular sequences is the one sometimes referred to as {\it Kasner polygons}. Given a polygon $K$, the first Kasner descendant $K'$ of $K$ is obtained by placing the vertices of $K'$ at the midpoints of the edges of $K$. More generally, for any fixed $m$ in $(0,1)$ one may define a sequence of polygons $\{K^{t}\}_{t\ge 0}$ where each polygon $K^{t}$ is obtained by dividing every edge of $K^{t-1}$ into the ratio $m:(1-m)$ in the counterclockwise (or clockwise) direction and taking these division points to be the vertices of $K^{t}$. We are interested in the following problem {\it Let $m$ be a fixed number in $(0,1)$ and let $n\ge 3$ be a fixed integer. Further, let $K$ be a convex $n$-gon and denote by $K'$, the first $m$-Kasner descendant of $K$, that is, the vertices of $K'$ divide the edges of $K$ into the ratio $m:(1-m)$. What can be said about the ratio between the area of $K'$ and the area of $K$, when $K$ varies in the class of convex $n$-gons?} We provide a complete answer to this question.

math.MG↗