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Janusz A. Hołyst

Publications and source records attributed to Janusz A. Hołyst.

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↗

From equality to diversity -- bottom-up approach for hierarchy growth

The hierarchical topology is a common property of many complex systems. Here we introduce a simple but generic model of hierarchy growth from the bottom to the top. Therein, two dynamical processes are accounted for: agent's promotions to next hierarchy levels when local speakers are elected and followed by other agents and agent's degradations to the lowest hierarchy. Following the initial stage when all agents are at the bottom level in the course of time the system approaches a stationary state where new hierarchies no longer emerge and the distribution of agents at different levels is exponential. In the stationary state the average hierarchy level and the fraction of agents at the lowest level are independent from the system size however the height of hierarchy, i.e. maximal number of observed hierarchy levels grows logarithmically along the total number of agents. The average number of followers of an agent in the stationary state is much smaller than the number of followers he possessed at the promotion moment. Results from numerical simulations are confirmed by an analytical treatment based on the rate equation.

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↗

Mean-field approximation for structural balance dynamics in heat-bath

A critical temperature for a complete signed graph of $N$ agents where time-dependent links polarization tends towards the Heider (structural) balance is found analytically using the heat-bath approach and the mean-field approximation as $T^c=(N-2)/a^c$, where $a^c\approx 1.71649$. The result is in perfect agreement with numerical simulations starting from the paradise state where all links are positively polarized as well as with the estimation of this temperature received earlier with much more sophisticated methods. When heating the system, one observes a discontinuous and irreversible phase transition at $T^c$ from a nearly balanced state when the mean link polarization is about $x_c=0.796388$ to a disordered and unbalanced state where the polarization vanishes. When the initial conditions for links polarization are random, then at low temperatures a balanced bipolar state of two mutually hostile cliques exists that decays towards the disorder and there is a discontinuous phase transition at a temperature $T^d$ that is lower than $T^c$. The system phase diagram corresponds to the so-called fold catastrophe when a stable solution of the mean-field equation collides with a separatrix, and as a result a hysteresis-like loop is observed.

physics.soc-ph↗

Transitions between polarization and radicalization in a temporal bilayer echo chambers model

Echo chambers and polarisation dynamics are as of late a very prominent topic in scientific communities around the world. As these phenomena directly affect our lives and seemingly more and more as our societies and communication channels evolve it becomes ever so important for us to understand the intricacies of opinion dynamics in the modern era. Here we extend an existing echo chambers model with activity driven agents onto a bi-layer topology and study the dynamics of the polarised state as a function of interlayer couplings. Different cases of such couplings are presented - unidirectional coupling that can be reduced to a mono-layer facing an external bias, symmetric and non-symmetric couplings. We have assumed that initial conditions impose system polarisation and agent opinions are different for both layers. Such a pre-conditioned polarised state can sustain without explicit homophilic interactions provided the coupling strength between agents belonging to different layers is weak enough. For a strong unidirectional or attractive coupling between two layers a discontinuous transition to a radicalised state takes place when mean opinions in both layers are the same. When coupling constants between the layers are of different signs the system exhibits sustained or decaying oscillations. Transitions between these states are analysed using a mean field approximation and classified in the framework of bifurcation theory.

physics.soc-ph↗

Discovering hidden layers in quantum graphs

Finding hidden layers in complex networks is an important and a non-trivial problem in modern science. We explore the framework of quantum graphs to determine whether concealed parts of a multi-layer system exist and if so then what is their extent, i.e., how many unknown layers there are. Assuming that all information available is the time evolution of a wave propagation on a single layer of a network it is indeed possible to uncover that which is hidden by merely observing the dynamics. We present evidence on both synthetic and real-world networks that the frequency spectrum of the wave dynamics can express distinct features in the form of additional frequency peaks. These peaks exhibit dependence on the number of layers taking part in the propagation and thus allowing for the extraction of said number. We show that in fact, with sufficient observation time, one can fully reconstruct the row-normalised adjacency matrix spectrum. We compare our propositions to a machine learning approach using a modified, for the purposes of multi-layer systems, wave packet signature method.

cond-mat.dis-nn↗

Source location on multilayer networks

Nowadays it is not uncommon to have to deal with dissemination on multi-layered networks and often finding the source of said propagation can be a crucial task. In this paper we tackle this exact problem with a maximum likelihood approach that we extend to be operational on multi-layered graphs. We test our method for source location estimation on synthetic networks and outline its potential strengths and limitations. We also observe some non-trivial and perhaps surprising phenomena where the more of the system one observes the worse the results become whereas increased problem complexity in the form of more layers can actually improve our performance.

cs.SI↗

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↗

Optimizing sensors placement in complex networks for localization of hidden signal source: A review

As the world becomes more and more interconnected, our everyday objects become part of the Internet of Things, and our lives get more and more mirrored in virtual reality, where every piece of~information, including misinformation, fake news and malware, can spread very fast practically anonymously. To suppress such uncontrolled spread, efficient computer systems and algorithms capable to~track down such malicious information spread have to be developed. Currently, the most effective methods for source localization are based on sensors which provide the times at which they detect the~spread. We investigate the problem of the optimal placement of such sensors in complex networks and propose a new graph measure, called Collective Betweenness, which we compare against four other metrics. Extensive numerical tests are performed on different types of complex networks over the wide ranges of densities of sensors and stochasticities of signal. In these tests, we discovered clear difference in comparative performance of the investigated optimal placement methods between real or scale-free synthetic networks versus narrow degree distribution networks. The former have a clear region for any given method's dominance in contrast to the latter where the performance maps are less homogeneous. We find that while choosing the best method is very network and spread dependent, there are two methods that consistently stand out. High Variance Observers seem to do very well for spread with low stochasticity whereas Collective Betwenness, introduced in this paper, thrives when the spread is highly unpredictable.

cs.SI↗

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↗

Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior

Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation).1 One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade.2 Multilayer networks, high-dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi-dimensional social networks has recently garnered the attention of the network science community.3 Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions.6 Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich datasets, giving more accurate and complete pictures of social interactions.

econ.GN↗

Homophily based on few attributes can impede structural balance

Two complementary mechanisms are thought to shape social groups: homophily between agents and structural balance in connected triads. Here we consider $N$ fully connected agents, where each agent has $G$ underlying attributes, and the similarity between agents in attribute space (i.e., homophily) is used to determine the link weight between them. To incorporate structural balance we use a triad-updating rule where only one attribute of one agent is changed intentionally in each update, but this also leads to accidental changes in link weights and even link polarities. The link weight dynamics in the limit of large $G$ is described by a Fokker-Planck equation from which the conditions for a phase transition to a fully balanced state with all links positive can be obtained. This "paradise state" of global cooperation is, however, difficult to achieve requiring $G > O(N^2)$ and $p>0.5$, where the parameter $p$ captures a willingness to consensus. Allowing edge weights to be a consequence of attributes naturally captures homophily and reveals that many real-world social systems would have a subcritical number of attributes necessary to achieve structural balance.

physics.soc-ph↗

Modelling of temporal fluctuation scaling in online news network with independent cascade model

We show that activity of online news outlets follows a temporal fluctuation scaling law and we recover this feature using an independent cascade model augmented with a varying hype parameter representing a viral potential of an original article. We use the Event Registry platform to track activity of over 10,000 news outlets in 11 different topics in the course of the year 2016. Analyzing over 22,000,000 articles, we found that fluctuation scaling exponents $α$ depend on time window size $Δ$ in a characteristic way for all the considered topics -- news outlets activities are partially synchronized for $Δ>15\mathrm{min}$ with a cross-over for $Δ=1\mathrm{day}$. The proposed model was run on several synthetic network models as well as on a network extracted from the real data. Our approach discards timestamps as not fully reliable observables and focuses on co-occurrences of publishers in cascades of similarly phrased news items. We make use of the Event Registry news clustering feature to find correlations between content published by news outlets in order to uncover common information propagation paths in published articles and to estimate weights of edges in the independent cascade model. While the independent cascade model follows the fluctuation scaling law with a trivial exponent $α=0.5$, we argue that besides the topology of the underlying cooperation network a temporal clustering of articles with similar hypes is necessary to qualitatively reproduce the fluctuation scaling observed in the data.

physics.soc-ph↗

Diffusion on hierarchical systems of weakly-coupled networks

We analyse diffusion dynamics on weakly-coupled networks (interconnected networks) by means of separation of time scales. Using an adiabatic approximation we reduced the system dynamics to a Markov chain with aggregated variables and derived a transport equation that is analogous to Fick's First Law and includes a driving force. Entropy production is a sum of microscopic entropy transport, which results from the particle's migration between networks of different topologies and macroscopic entropy production of the Markov chain. Equilibrium particles partition between different sub-networks depends only on internal sub-network parameters. Our framework, confirmed by numerical simulations, is also useful for considering diffusion in nested systems corresponding to hierarchical networks with several different time scales thus it can serve to uncover hidden hierarchy levels from observations of diffusion processes.

nlin.CD↗

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↗

The role of emotional variables in the classification and prediction of collective social dynamics

We demonstrate the power of data mining techniques for the analysis of collective social dynamics within British Tweets during the Olympic Games 2012. The classification accuracy of online activities related to the successes of British athletes significantly improved when emotional components of tweets were taken into account, but employing emotional variables for activity prediction decreased the classifiers' quality. The approach could be easily adopted for any prediction or classification study with a set of problem-specific variables.

cs.SI↗

A simple model of local prices and associated risk evaluation

A simple spin system is constructed to simulate dynamics of asset prices and studied numerically. The outcome for the distribution of prices is shown to depend both on the dimension of the system and the introduction of price into the link measure. For dimensions below 2, the associated risk is high and the price distribution is bimodal. For higher dimensions, the price distribution is Gaussian and the associated risk is much lower. It is suggested that the results are relevant to rare assets or situations where few players are involved in the deal making process.

q-fin.GN↗

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↗