arXiv subjects

Byungjoon Min

Publications and source records attributed to Byungjoon Min.

At least 19 recordsLinked to original sources

arXiv2026

$(k,n)$-core percolation on hypergraphs with anchor nodes

Hypergraphs describe higher-order interactions that involve more than a pair of nodes. A characteristic feature of hypergraphs is that their robustness can be strongly affected by the different roles of the nodes. Indeed, some nodes might be essential for a hyperedge's function, while others might not be. The loss of a single essential node completely destroys the hyperedge it belongs to, while the loss of a non-essential node has a buffering effect, inducing the hyperedge to simply reduce its size. In order to capture this phenomenology, we formulate a comprehensive theoretical framework for $(k,n)$-core percolation models on hypergraphs, where each node of a hyperedge is an anchor with probability $\theta$, and a hyperedge fails if an anchor node fails. Hypergraph $(k,n)$-core percolation problems can be classified as first-neighbor and second-neighbor problems, indicating that in the pruning process the connectivity is ensured only by the state of the first neighbors or the second neighbors, respectively. We derive self-consistency equations for first-neighbor and second-neighbor (node- and hyperedge-based) pruning processes, and obtain the size of the giant $(k,n)$-core. We obtain the phase diagram, including continuous and discontinuous transitions, and confirm our theory on random hypergraphs using numerical simulations. The results show how the heterogeneity of the nodes' functional roles and the extended range of the interactions affect the robustness of higher-order networks.

physics.soc-ph↗

arXiv2026

Giant strongly biconnected components of directed networks: a generating function approach

Strongly connected components (SCCs) characterize modular structure in directed networks but are fragile to single node failures. We study strongly biconnected components (SBCs), which are the set of nodes in which every node pair remains mutually reachable after the removal of any single node, as a more robust notion of connectivity. Using a generating function formalism, we derive the size of the giant SBC and analyze its percolation behavior under random node and link removal. We show that the giant SBC emerges at the same threshold as the giant SCC but grows more slowly due to stricter connectivity requirements. We also applied our theoretical framework to real-world biological networks including gene regulatory networks and neural connectome. Our framework provides insight into the interplay between connectivity, redundancy, and robustness in complex directed systems.

physics.soc-ph↗

arXiv2026

Dynamical processes and emergent behaviors in multiplex networks

Over the last two decades, network science has greatly advanced our understanding of how the collective behaviors of a complex system emerge from the interactions among its basic units. Multiplex networks, i.e. networks with many layers, whose nodes are in one-to-one correspondence, provide a more realistic description for social, biological and ecological systems where multiple types of interactions coexist. After a brief introduction on how to model the architecture of multiplex networks, we present a complete overview of the different dynamics which can unfold over these structures. We present a unified framework to describe dynamical processes such as percolation, reaction-diffusion, synchronization, epidemic spreading, social dynamics and games on multiplex networks, as well as the coupled evolution of different dynamical processes, and the coevolution of a process with the network structure. Our focus is on truly-multiplex collective behaviors, i.e., all those phenomena which cannot emerge on the corresponding aggregated networks, or when the different layers of these systems are considered in isolation. We identify three main mechanisms leading to new collective behaviors: the existence of structural correlations across layers, the presence of dynamical correlations in the processes taking place at the different layers, and the dynamical interplay of inter- and intra-layer interactions. We conclude with a summary of the main takeaways from a decade of work in the field.

physics.soc-ph↗

arXiv2026

Feedback percolation on complex networks

Traditional percolation theory assumes static microscopic rules, limiting its ability to describe real-world complex systems where macroscopic order actively regulates local interactions. Here, we introduce feedback percolation, an unified framework that dynamically couples the microscopic activation probability to the macroscopic size of the giant component. We show that this simple feedback mechanism produces a rich variety of behaviors both analytically and numerically. Depending on the feedback functions, the system exhibits explosive discontinuous jumps, hybrid transitions, limit-cycle oscillations, and routes to chaos, absent in classical percolation. Our findings establish that macroscopic feedback provides a unifying physical mechanism for phenomena ranging from self-regulating oscillations to systemic infrastructure collapse.

cond-mat.stat-mech↗

arXiv2025

Dynamics of attractor transitions in Boolean networks under noise

Biological systems operate under persistent noise, which can alter system states and induce transitions between attractors. Here, we study the attractor dynamics of Boolean networks focusing on the transitions between attractors induced by noise. By computing transition probabilities between attractors, we present methods at the attractor level to determine dominance, stability, and diversity of attractors, and systematically compare local and global noise. Whereas global noise leads to attractor behavior dictated primarily by basin sizes, local noise produces structured transition patterns characterized by enhanced stability, non-trivial dominance patterns, and broader exploration of the attractor space. Our work offers insight into the dynamics of attractors, showing the importance of transition patterns under noise.

q-bio.MN↗

arXiv2025

Spatially explicit population modeling with habitat preferential movement of Glis glis (Edible dormouse)

In this study, we present a population dispersal model for Glis glis, a rodent species with a strong preference for forest habitats. The model addresses dispersal processes by incorporating the ecological traits, including habitat preferences of the species, potential growth rate, home range, and carrying capacity. In this model, the landscape is divided into spatial units based on the home range, and the probability of individuals moving from one spatial unit to another is calculated using habitat preference and population density. The movement probability between different spatial locations is determined by the product of two factors: the escape probability based on the relative ratio of population density to carrying capacity and the relative difference in habitat preferences. Both probabilities are calculated using logistic functions. The results indicate that the combination of habitat preference and local density dependence plays a role in shaping the dispersal patterns of G. glis. This highlights the importance of considering habitat preference and density-dependent effects concurrently when forecasting population dispersal under field conditions.

q-bio.PE↗

arXiv2025

Analysis of a voter model with an evolving number of opinion states

In traditional voter models, opinion dynamics are driven by interactions between individuals, where an individual adopts the opinion of a randomly chosen neighbor. However, these models often fail to capture the emergence of entirely new opinions, which can arise spontaneously in real-world scenarios. Our study introduces a novel element to the classic voter model: the concept of innovation, where individuals have a certain probability of generating new opinions independently of their neighbors' states. This innovation process allows for a more realistic representation of social dynamics, where new opinions can emerge and old ones may fade over time. Through analytical and numerical analysis, we find that the balance between innovation and extinction shapes the number of opinions in the steady state. Specifically, for low innovation rates, the system tends toward near-consensus, while higher innovation rates lead to greater opinion diversity. We also show that network structure influences opinion dynamics, with greater degree heterogeneity reducing the number of opinions in the system.

physics.soc-ph↗

arXiv2025

Aging in coevolving voter models

Aging, understood as the tendency to remain in a given state the longer the persistence time in that state, plays a crucial role in the dynamics of complex systems. In this paper, we explore the influence of aging on coevolution models, that is, models in which the dynamics of the states of the nodes in a complex network is coupled to the dynamics of the structure of the network. In particular we consider the coevolving voter model, and we introduce two versions of this model that include aging effects: the Link Aging Model (LAM) and the Node Aging Model (NAM). In the LAM, aging is associated with the persistence time of a link in the evolving network, while in the NAM, aging is associated with the persistence time of a node in a given state. We show that aging significantly affects the absorbing phase transition of the coevolution voter model, shifting the transition point in opposite directions for the LAM and NAM. We also show that the generic absorbing phase transition can disappear due to aging effects.

physics.soc-ph↗

arXiv2024

Competition between group interactions and nonlinearity in voter dynamics on hypergraphs

Social dynamics are often driven by both pairwise (i.e., dyadic) relationships and higher-order (i.e., polyadic) group relationships, which one can describe using hypergraphs. To gain insight into the impact of polyadic relationships on dynamical processes on networks, we formulate and study a polyadic voter process, which we call the group-driven voter model (GVM), that incorporates the effect of group interactions by nonlinear interactions that are subject to a group (i.e., hyperedge) constraint. By examining the competition between nonlinearity and group sizes, we show that the GVM achieves consensus faster than standard voter-model dynamics, with an optimal minimizing exit time. We substantiate this finding by using mean-field theory on annealed uniform hypergraphs with $N$ nodes, for which the exit time scales as ${\cal A}\ln N$, where the prefactor ${\cal A}$ depends both on the nonlinearity and on group-constraint factors. Our results reveal how competition between group interactions and nonlinearity shapes GVM dynamics. We thereby highlight the importance of such competing effects in complex systems with polyadic interactions.

physics.soc-ph↗

arXiv2024

No-exclaves percolation on random networks

No-exclaves percolation (NExP) is a nonlocal percolation process in which the components are formed not only by the connected occupied nodes but also by the agglomeration of empty nodes completely surrounded by the occupied nodes. It has been studied in low dimensions, displaying such novel phenomena as the discontinuous transition to complete percolation. However, its characteristics in complex networks are still unexplored. In this paper, we study the NExP on random networks by developing mean-field solutions using the generating function formalism. Our theory allows us to determine the size of the giant no-exclaves component as well as the percolation threshold, which are in excellent agreements with Monte Carlo simulations on random networks and some real-world networks. We show that on random networks NExP exhibits three phases and two transitions between them: the phases are characterized by the presence or absence of not only the giant NExP component but also the giant unoccupied component, which is the giant connected component composed solely of unoccupied nodes. This work offers theoretical understanding on the anatomy of phase transitions in the NExP process.

cond-mat.stat-mech↗

arXiv2023

Coevolutionary Dynamics of Group Interactions: Coevolving Nonlinear Voter Models

We survey the coevolutionary dynamics of network topology and group interactions in opinion formation, grounded on a coevolving nonlinear voter model. The coevolving nonlinear voter model incorporates two mechanisms: group interactions implemented through nonlinearity in the voter model and network plasticity demonstrated as the rewiring of links to remove connections between nodes in different opinions. We show that the role of group interactions, implemented by the nonlinearity can significantly impact both the dynamical outcomes of nodes' state and the network topology. Additionally, we review several variants of the coevolving nonlinear voter model considering different rewiring mechanisms, noise of flipping nodes' state, and multilayer structures. We portray the various aspects of the coevolving nonlinear voter model as an example of network coevolution driven by group interactions, and finally, present the implications and potential directions for future research.

physics.soc-ph↗

arXiv2023

Coevolutionary dynamics of information spreading and heterophilic link rewiring

In many complex systems, the dynamic processes that take place on a network and the changes in the network topology are intertwined. Here, we propose a model of coevolutionary dynamics of information spreading which is accompanied with link rewiring to facilitate the propagation of information. In our model, nodes possessing information attempt to contact new susceptible nodes through the link rewiring while the information spreads on a network. Using moment-closure and heterogeneous mean-field approximations, we examine both the information spread dynamics and network evolution focusing on epidemic size, epidemic threshold, and degree distributions at the steady state. We found that more frequent heterophilic link rewiring leads to a larger epidemic size but does not alter the epidemic threshold. We also observed that link rewiring results in a broader degree distribution in the steady state. This study provides an insight into the the role of the heterophilic link rewiring in both facilitating information propagation and inducing network heterogeneity.

physics.soc-ph↗

arXiv2023

Threshold cascade dynamics on coevolving networks

We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and the network plasticity implemented as rewiring of links to cut the connections between nodes in different states. Using numerical simulations and a mean-field theoretical analysis, we demonstrate that the coevolutionary dynamics can significantly affect the cascades dynamics. The domain of parameters, i.e., threshold and network mean degree, for which global cascades occur shrinks with increasing network plasticity, indicating that the rewiring process suppresses the onset of global cascades. We also found that during evolution, non-adopted nodes form denser connections, resulting in a wider degree distribution and a non-monotonous dependence of cascades sizes with plasticity.

physics.soc-ph↗

arXiv2023

Critical behaviors of cascading dynamics on multiplex two-dimensional lattices

We study the critical phenomena of viable clusters in multiplex two-dimensional lattices using numerical simulations. We identify viable sites on multiplex lattices using two cascading algorithms: the cascade of activations (CA) and deactivations (CD). We found that the giant viable clusters identified by CA and CD processes exhibit different critical behaviors. Specifically, the critical phenomena of CA processes are consistent with the ordinary bond percolation on a single layer but CD processes exhibit the critical behaviors consistent with mutual percolation on multiplex lattices. In addition, we computed the susceptibility of cascading dynamics by using the concept of ghost field. Our results suggest that the CA and CD processes generate viable clusters in different ways.

cond-mat.stat-mech↗

arXiv2023

Threshold cascade dynamics on signed random networks

Relationships between individuals in a social network, genes in biological systems, and spins in magnetic systems often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. However, most studies of complex networks have focused on networks consisting of solely positive interactions. Here, we study threshold cascades on signed networks composed of both positive and negative connections, focusing on when a pair of nodes connected by a negative link can only be activated exclusively to each other. We found that the negative interactions not only suppress global cascades, but also induce the heterogeneity in activation patterns manifesting from single-node to network levels. Our results suggest that negative interactions may be an important source of the variability in cascading dynamics.

physics.soc-ph↗

arXiv2022

Identifying influential subpopulations in metapopulation epidemic models using message-passing theory

Identifying influential subpopulations in metapopulation epidemic models has far-reaching potential implications for surveillance and intervention policies of a global pandemic. However, there is a lack of methods to determine influential nodes in metapopulation models based on a rigorous mathematical background. In this study, we derive the message-passing theory for metapopulation modeling and propose a method to determine influential spreaders. Based on our analysis, we identify the most dangerous city as a potential seed of a pandemic when applied to real-world data. Moreover, we particularly assess the relative importance of various sources of heterogeneity at the subpopulation level, e.g., the number of connections and mobility patterns, to determine properties of spreading processes. We validate our theory with extensive numerical simulations on empirical and synthetic networks considering various mobility and transmission probabilities. We confirm that our theory can accurately predict influential subpopulations in metapopulation models.

physics.soc-ph↗

arXiv2021

Double transitions and hysteresis in heterogeneous contagion processes

In many real-world contagion phenomena, the number of contacts to spreading entities for adoption varies for different individuals. Therefore, we study a model of contagion dynamics with heterogeneous adoption thresholds. We derive mean-field equations for the fraction of adopted nodes and obtain phase diagrams in terms of the transmission probability and fraction of nodes requiring multiple contacts for adoption. We find a double phase transition exhibiting a continuous transition and a subsequent discontinuous jump in the fraction of adopted nodes because of the heterogeneity in adoption thresholds. Additionally, we observe hysteresis curves in the fraction of adopted nodes owing to adopted nodes in the densely connected core in a network.

physics.soc-ph↗

arXiv2021

Interplay between degree and Boolean rules in the stability of Boolean networks

Empirical evidence has revealed that biological regulatory systems are controlled by high-level coordination between topology and Boolean rules. In this study, we study the joint effects of degree and Boolean functions on the stability of Boolean networks. To elucidate these effects, we focus on i) the correlation between the sensitivity of Boolean variables and the degree, and ii) the coupling between canalizing inputs and degree. We find that negatively correlated sensitivity with respect to local degree enhances the stability of Boolean networks against external perturbations. We also demonstrate that the effects of canalizing inputs can be amplified when they coordinate with high in-degree nodes. Numerical simulations confirm the accuracy of our analytical predictions at both the node and network levels.

nlin.AO↗