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Krzysztof Suchecki

Publications and source records attributed to Krzysztof Suchecki.

At least 19 recordsLinked to original sources

Structurally balanced growing network as randomized Pólya urn process

We investigate a process of growth of a signed network that strictly adheres to Heider structural balance rules, resulting in two opposing, growing factions. New agents make contact with a random existing agent and join one of the factions with the bias $p$ towards the group they made contact with. The evolution of the group sizes can be mapped to a randomized Pólya urn model. Aside from $p=1$, the relative sizes of the two factions always tend towards $1/2$, but the behavior differs in the anti-bias regime ($p<1/2$) and the biased one ($p>1/2$). In the anti-bias regime, the expected faction sizes converge toward equality, regardless of initial differences, while in the biased regime, initial size difference persists over time. This difference is obscured by fluctuations, with the faction size distribution remaining unimodal even above $p>1/2$, up until a characteristic point $p^{ch}$, where it becomes bimodal, with initially larger and smaller factions featuring their own distinguishable peaks. We discuss several approaches to estimate this characteristic value. At $p=1$, differences between the relative sizes of factions can persist indefinitely, although still subject to fluctuations.

physics.soc-ph↗

Biswas-Chatterjee-Sen kinetic exchange opinion model for two connected groups

We consider a kinetic model of opinion dynamics known as the Biswas-Chatterjee-Sen model with a modular interaction structure. The system consists of two groups of agents that feature more frequent interactions within each group and rarer interactions between agents of different groups. We use the mean-field analytical approximation to determine that aside from previously known ordered and disordered states, a new antisymmetric ordered state is stable, where each group has an opposite dominant opinion. The limits of system interaction strength and noise for the stability of such a state are determined, with a discontinuous transition from an antisymmetric to a symmetric state happening if thresholds are exceeded. The results of numerical agent-based simulations confirm our analytical predictions and show that the critical values of noise and interaction strength are predicted with good accuracy.

physics.soc-ph↗

Paradise-disorder transition in structural balance dynamics on Erdös-Rényi graphs

Structural balance has been posited as one of the factors influencing how friendly and hostile relations of social actors evolve over time. This study investigates the behavior of the Heider balance model in Erdös-Rényi random graphs in the presence of a noisy environment, particularly the transition from an initially entirely positively polarized paradise state to a disordered phase. We examine both single-layer and bilayer network configurations and provide a mean-field solution for the average link polarization that predicts a first-order transition where the critical temperature scales with the connection probability $p$ as $p^2$ for a monolayer system and in a more complex way for a bilayer. We show that to mimic the dynamics observed in complete graphs, the intralayer Heider interaction strengths should be scaled as $p^{-2}$, while the interlayer interaction strengths should be scaled as $p^{-1}$ for random graphs. Numerical simulations have been performed, and their results confirm our analytical predictions, provided that graphs are dense enough.

physics.soc-ph↗

Critical properties of Heider balance on multiplex networks

Heider's structural balance theory has proven invaluable in comprehending the dynamics of social groups characterized by both friendly and hostile relationships. Since people's relations are rarely single-faceted, we investigate Heider balance dynamics on a multiplex network, consisting of several copies of the same agent displaying correlated relations at different layers building the multiplex. Intralayer interactions in our model adhere to Heider dynamics, while interlayer correlations stem from Ising interactions, with the heat bath dynamics of link signs. The investigations uncover a multifaceted system with a diverse equilibrium landscape contingent on the coexistence of distinct phases across layers. We observe that starting from a paradise state with positive links in all layers, an increase in temperature triggers a discontinuous transition to a disordered state akin to single-layer scenarios. The critical temperature surpasses that of the single-layer case, a fact verified through extended mean-field analysis and agent-based simulations. Furthermore, the scenario shifts when one layer exhibits a two-clique configuration instead of a paradise state. This change introduces additional transitions: synchronization of inter-layer relations and a transition to the disorder, appearing at a different, lower temperature compared to matching paradise states. This exploration shows the intricate interplay of Heider balance and multiplex interactions.

physics.soc-ph↗

Locating the source of interacting signal in complex networks

We investigate the problem of locating the source of a self-interacting signal spreading in a complex networks. We use a well-known rumour model as an example of the process with self-interaction. According to this model based on the SIR epidemic dynamics, the infected nodes may interact and discourage each other from gossiping with probability $α$. We compare three algorithms of source localization: Limited Pinto-Thiran-Vettarli (LPTV), Gradient Maximum Likelihood (GMLA) and one based on Pearson correlation between time and distance. The results of numerical simulations show that additional interactions between infected nodes decrease the quality of LPTV and Pearson. GMLA is the most resistant to harmful effects of the self-interactions, which is especially visible for medium and high level of stochasticity of the process, when spreading rate is below 0.5. The reason for this may be the fact that GMLA uses only the nearest observers, which are much less likely affected by the interactions between infected nodes, because these contacts become important as the epidemics develops and the number of infected agents increases.

physics.soc-ph↗

Spontaneous symmetry breaking of active phase in coevolving nonlinear voter model

We study an adaptive network model driven by a nonlinear voter dynamics. Each node in the network represents a voter and can be in one of two states that correspond to different opinions shared by the voters. A voter disagreeing with its neighbor's opinion may either adopt it or rewire its link to another randomly chosen voter with any opinion. The system is studied by means of the pair approximation in which a distinction between the average degrees of nodes in different states is made. This approach allows us to identify two dynamically active phases, a symmetric and an asymmetric one. The asymmetric active phase, in contrast to the symmetric, is characterized by different numbers of nodes in the opposite states that coexist in the network. The pair approximation predicts the possibility of spontaneous symmetry breaking, which leads to a continuous phase transition between the symmetric and the asymmetric active phases. In this case, the absorbing transition occurs between the asymmetric active and the absorbing phases after the spontaneous symmetry breaking. Discontinuous phase transitions and hysteresis loops between both active phases are also possible. Interestingly, the asymmetric active phase is not displayed by the model where the rewiring occurs only to voters sharing the same opinion, studied by other authors. Our results are backed up by Monte Carlo simulations.

physics.soc-ph↗

Multiple propagation paths enhance locating the source of diffusion in complex networks

We investigate the problem of locating the source of diffusion in complex networks without complete knowledge of nodes' states. Some currently known methods assume the information travels via a single, shortest path, which by assumption is the fastest way. We show that such a method leads to the overestimation of propagation time for synthetic and real networks, where multiple shortest paths as well as longer paths between vertices exist. We propose a new method of source estimation based on maximum likelihood principle, that takes into account existence multiple shortest paths. It shows up to 1.6 times higher accuracy in synthetic and real networks.

physics.soc-ph↗

Coupling of link- and node-ordering in the coevolving voter model

We consider the process of reaching the final state in the coevolving voter model. There is a coevolution of state dynamics, where a node can copy a state from a random neighbor with probabilty $1-p$ and link dynamics, where a node can re-wire its link to another node of the same state with probability $p$. That exhibits an absorbing transition to a frozen phase above a critical value of rewiring probability. Our analytical and numerical studies show that in the active phase mean values of magnetization of nodes $n$ and links $m$ tend to the same value that depends on initial conditions. In a similar way mean degrees of spins up and spins down become equal. The system obeys a special statistical conservation law since a linear combination of both types magnetizations averaged over many realizations starting from the same initial conditions is a constant of motion: $Λ\equiv (1-p)μm(t)+pn(t) = const$, where $μ$ is the mean node degree. The final mean magnetization of nodes and links in the active phase is proportional to $Λ$ while the final density of active links is a square function of $Λ$. If the rewiring probability is above a critical value and the system separates into disconnected domains, then the values of nodes and links magnetizations are not the same and final mean degrees of spins up and spins down can be different.

physics.soc-ph↗

Models of random graph hierarchies

We introduce two models of inclusion hierarchies: Random Graph Hierarchy (RGH) and Limited Random Graph Hierarchy (LRGH). In both models a set of nodes at a given hierarchy level is connected randomly, as in the Erdős-Rényi random graph, with a fixed average degree equal to a system parameter $c$. Clusters of the resulting network are treated as nodes at the next hierarchy level and they are connected again at this level and so on, until the process cannot continue. In the RGH model we use all clusters, including those of size $1$, when building the next hierarchy level, while in the LRGH model clusters of size $1$ stop participating in further steps. We find that in both models the number of nodes at a given hierarchy level $h$ decreases approximately exponentially with $h$. The height of the hierarchy $H$, i.e. the number of all hierarchy levels, increases logarithmically with the system size $N$, i.e. with the number of nodes at the first level. The height $H$ decreases monotonically with the connectivity parameter $c$ in the RGH model and it reaches a maximum for a certain $c_{max}$ in the LRGH model. The distribution of separate cluster sizes in the LRGH model is a power law with an exponent about $-1.25$. The above results follow from approximate analytical calculations and have been confirmed by numerical simulations.

physics.soc-ph↗

Hierarchical Cont-Bouchaud model

We extend the well-known Cont-Bouchaud model to include a hierarchical topology of agent's interactions. The influence of hierarchy on system dynamics is investigated by two models. The first one is based on a multi-level, nested Erdos-Renyi random graph and individual decisions by agents according to Potts dynamics. This approach does not lead to a broad return distribution outside a parameter regime close to the original Cont-Bouchaud model. In the second model we introduce a limited hierarchical Erdos-Renyi graph, where merging of clusters at a level h+1 involves only clusters that have merged at the previous level h and we use the original Cont-Bouchaud agent dynamics on resulting clusters. The second model leads to a heavy-tail distribution of cluster sizes and relative price changes in a wide range of connection densities, not only close to the percolation threshold.

physics.soc-ph↗

Is the Voter Model a model for voters?

The voter model has been studied extensively as a paradigmatic opinion dynamics' model. However, its ability for modeling real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with agents' recurrent mobility (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anysotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of the US presidential elections as the stationary vote-share fluctuations across counties, and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when a real-space renormalization is performed by coarse-graining the geographical scale from county level through congressional districts and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making which are consistent with the empirical observations.

physics.soc-ph↗

Information slows down hierarchy growth

We consider models of growing multi-level systems wherein the growth process is driven by rules of tournament selection. A system can be conceived as an evolving tree with a new node being attached to a contestant node at the best hierarchy level (a level nearest to the tree root). The proposed evolution reflects limited information on system properties available to new nodes. It can also be expressed in terms of population dynamics. Two models are considered: a constant tournament (CT) model wherein the number of tournament participants is constant throughout system evolution, and a proportional tournament (PT) model where this number increases proportionally to the growing size of the system itself. The results of analytical calculations based on a rate equation fit well to numerical simulations for both models. In the CT model all hierarchy levels emerge but the birth time of a consecutive hierarchy level increases exponentially or faster for each new level. The number of nodes at the first hierarchy level grows logarithmically in time, while the size of the last, "worst" hierarchy level oscillates quasi log-periodically. In the PT model the occupations of the first two hierarchy levels increase linearly but worse hierarchy levels either do not emerge at all or appear only by chance in early stage of system evolution to further stop growing at all. The results allow to conclude that information available to each new node in tournament dynamics restrains the emergence of new hierarchy levels and that it is the absolute amount of information, not relative, which governs such behavior.

physics.soc-ph↗

Finite size induces crossover temperature in growing spin chains

We introduce a growing one-dimensional quenched spin model that bases on asymmetrical one-side Ising interactions in the presence of external field. Numerical simulations and analytical calculations based on Markov chain theory show that when the external field is smaller than the exchange coupling constant $J$ there is a non-monotonous dependence of the mean magnetization on the temperature in a finite system. The crossover temperature $T_c$ corresponding to the maximal magnetization decays with system size, approximately as the inverse of the W Lambert function. The observed phenomenon can be understood as an interplay between the thermal fluctuations and the presence of the first cluster determined by initial conditions. The effect exists also when spins are not quenched but fully thermalized after the attachment to the chain. We conceive the model is suitable for a qualitative description of online emotional discussions arranged in a chronological order, where a spin in every node conveys emotional valence of a subsequent post.

physics.soc-ph↗

Hierarchy depth in directed networks

We explore depth measures for flow hierarchy in directed networks. We define two measures -- rooted depth and relative depth, and discuss differences between them. We investigate how the two measures behave in random Erdos-Renyi graphs of different sizes and densities and explain obtained results.

physics.soc-ph↗

The evolution of classification systems: Ontogeny of the UDC

To classify is to put things in meaningful groups, but the criteria for doing so can be problematic. Study of evolution of classification includes ontogenetic analysis of change in classification over time. We present an empirical analysis of the UDC over the entire period of its development. We demonstrate stability in main classes, with major change driven by 20th century scientific developments. But we also demonstrate a vast increase in the complexity of auxiliaries. This study illustrates an alternative to Tennis' "scheme-versioning" method.

cs.DL↗

Evolution of Wikipedia's Category Structure

Wikipedia, as a social phenomenon of collaborative knowledge creating, has been studied extensively from various points of views. The category system of Wikipedia, introduced in 2004, has attracted relatively little attention. In this study, we focus on the documentation of knowledge, and the transformation of this documentation with time. We take Wikipedia as a proxy for knowledge in general and its category system as an aspect of the structure of this knowledge. We investigate the evolution of the category structure of the English Wikipedia from its birth in 2004 to 2008. We treat the category system as if it is a hierarchical Knowledge Organization System, capturing the changes in the distributions of the top categories. We investigate how the clustering of articles, defined by the category system, matches the direct link network between the articles and show how it changes over time. We find the Wikipedia category network mostly stable, but with occasional reorganization. We show that the clustering matches the link structure quite well, except short periods preceding the reorganizations.

physics.soc-ph↗

Need to categorize: A comparative look at the categories of the Universal Decimal Classification system (UDC) and Wikipedia

This study analyzes the differences between the category structure of the Universal Decimal Classification (UDC) system (which is one of the widely used library classification systems in Europe) and Wikipedia. In particular, we compare the emerging structure of category-links to the structure of classes in the UDC. With this comparison we would like to scrutinize the question of how do knowledge maps of the same domain differ when they are created socially (i.e. Wikipedia) as opposed to when they are created formally (UDC) using classificatio theory. As a case study, we focus on the category of "Arts".

cs.DL↗

Highly connected - a recipe for success

In this paper, we tackle the problem of innovation spreading from a modeling point of view. We consider a networked system of individuals, with a competition between two groups. We show its relation to the innovation spreading issues. We introduce an abstract model and show how it can be interpreted in this framework, as well as what conclusions we can draw form it. We further explain how model-derived conclusions can help to investigate the original problem, as well as other, similar problems. The model is an agent-based model assuming simple binary attributes of those agents. It uses a majority dynamics (Ising model to be exact), meaning that individuals attempt to be similar to the majority of their peers, barring the occasional purely individual decisions that are modeled as random. We show that this simplistic model can be related to the decision-making during innovation adoption processes. The majority dynamics for the model mean that when a dominant attribute, representing an existing practice or solution, is already established, it will persists in the system. We show however, that in a two group competition, a smaller group that represents innovation users can still convince the larger group, if it has high self-support. We argue that this conclusion, while drawn from a simple model, can be applied to real cases of innovation spreading. We also show that the model could be interpreted in different ways, allowing different problems to profit from our conclusions.

physics.soc-ph↗