arXiv subjects

Bosiljka Tadic

Publications and source records attributed to Bosiljka Tadic.

At least 37 records · Page 2Linked to original sources

arXiv2015

The influence of architecture on collective charge transport in nanoparticle assemblies revealed by the fractal time series and topology of phase space manifolds

Charge transport within Coulomb blockade regime in two-dimensional nanoparticle arrays exhibits nonlinear I-V characteristics, where the level of nonlinearity strongly associates with the array's architecture. Here, we use different mathematical approaches to quantify collective behavior in the charge transport inside the sample and its relationship to the structural characteristics of the assembly and the presence of charge disorder. In particular, we simulate single-electron tunneling conduction in several assemblies with controlled variation of the structural components (branching, extended linear segments) that influence the local communication among the conducting paths between the electrodes. Furthermore, by applying the fractal analysis of time series of the number of tunnelings and the technique of algebraic topology, we unravel the temporal correlations and structure of the phase-space manifolds corresponding to the cooperative fluctuations of charge. By tracking the I-V curves in different assemblies together with the indicators of collective dynamics and topology of manifolds in the state space, we show that the increased I-V nonlinearity is fully consistent with the enhanced aggregate fluctuations and topological complexity of the participating states. The architecture that combines local branching and global topological disorder enables the creation of large drainage basins of nano-rivers leading to stronger cooperation effects. Also, by determining shifts in the topology and collective transport features, we explore the impact of the size of electrodes and local charge disorder. The results are relevant for designing the nanoparticle devices with improved conduction; they also highlight the significance of topological descriptions for a broader understanding of the nature of fluctuations at the nanoscale.

cond-mat.mes-hall↗

arXiv2014

The Dynamics of Emotional Chats with Bots: Experiment and Agent-Based Simulations

Quantitative research of emotions in psychology and machine-learning methods for extracting emotion components from text messages open an avenue for physical science to explore the nature of stochastic processes in which emotions play a role, e.g., in human dynamics online. Here, we investigate the occurrence of collective behavior of users that is induced by chats with emotional Bots. The Bots, designed in an experimental environment, are considered. Furthermore, using the agent-based modeling approach, the activity of these experimental Bots is simulated within a social network of interacting emotional agents. Quantitative analysis of time series carrying emotional messages by agents suggests temporal correlations and persistent fluctuations with clustering according to emotion similarity. {All data used in this study are fully anonymized.}

cs.SI↗

arXiv2013

Can Human-Like Bots Control Collective Mood: Agent-Based Simulations of Online Chats

Using agent-based modeling approach, in this paper, we study self-organized dynamics of interacting agents in the presence of chat Bots. Different Bots with tunable ``human-like'' attributes, which exchange emotional messages with agents, are considered, and collective emotional behavior of agents is quantitatively analysed. In particular, using detrended fractal analysis we determine persistent fluctuations and temporal correlations in time series of agent's activity and statistics of avalanches carrying emotional messages of agents when Bots favoring positive/negative affects are active. We determine the impact of Bots and identify parameters that can modulate it. Our analysis suggests that, by these measures, the emotional Bots induce collective emotion among interacting agents by suitably altering the fractal characteristics of the underlying stochastic process.Positive-emotion Bots are slightly more effective than the negative ones. Moreover, the Bots which are periodically alternating between positive and negative emotion, can enhance fluctuations in the system leading to the avalanches of agent's messages that are reminiscent of self-organized critical states.

physics.soc-ph↗

arXiv2012

Structure and stability of online chat networks built on emotion-carrying links

High-resolution data of online chats are studied as a physical system in laboratory in order to quantify collective behavior of users. Our analysis reveals strong regularities characteristic to natural systems with additional features. In particular, we find self-organized dynamics with long-range correlations in user actions and persistent associations among users that have the properties of a social network. Furthermore, the evolution of the graph and its architecture with specific k-core structure are shown to be related with the type and the emotion arousal of exchanged messages. Partitioning of the graph by deletion of the links which carry high arousal messages exhibits critical fluctuations at the percolation threshold.

physics.soc-ph↗

arXiv2012

How the online social networks are used: Dialogs-based structure of MySpace

Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors as user's psychology profiles and their reasons to use the online contacts. In this paper we have compiled and analyzed two datasets from \texttt{MySpace}. The data contain networked dialogs occurring within a specified time depth, high temporal resolution, and texts of messages, in which the emotion valence is assessed by using SentiStrength classifier. Performing a comprehensive analysis we obtain three groups of results: Dynamic topology of the dialogs-based networks have characteristic structure with Zipf's distribution of communities, low link reciprocity, and disassortative correlations. Overlaps supporting "weak-ties" hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion. Patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and nteractivity times longer than one day. Taken together, these results give a new insight into functioning of the online social networks and unveil importance of the amount of information and emotion that is communicated along the social links. (All data used in this study are fully anonymized.)

physics.soc-ph↗

arXiv2010

Modelling conflicts with cluster dynamics on networks

We introduce cluster dynamical models of conflicts in which only the largest cluster can be involved in an action. This mimics the situations in which an attack is planned by a central body, and the largest attack force is used. We study the model in its annealed random graph version, on a fixed network, and on a network evolving through the actions. The sizes of actions are distributed with a power-law tail, however, the exponent is non-universal and depends on the frequency of actions and sparseness of the available connections between units. Allowing the network reconstruction over time in a self-organized manner, e.g., by adding the links based on previous liaisons between units, we find that the power-law exponent depends on the evolution time of the network. Its lower limit is given by the universal value 5/2, derived analytically for the case of random fragmentation processes. In the temporal patterns behind the size of actions we find long-range correlations in the time series of number of clusters and non-trivial distribution of time that a unit waits between two actions. In the case of an evolving network the distribution develops a power-law tail, indicating that through the repeated actions, the system develops internal structure which is not just more effective in terms of the size of events, but also has a full hierarchy of units.

physics.soc-ph↗

arXiv2008

Collective Charge Fluctuations in Single-Electron Processes on Nano-Networks

Using numerical modeling we study emergence of structure and structure-related nonlinear conduction properties in the self-assembled nanoparticle films. Particularly, we show how different nanoparticle networks emerge within assembly processes with molecular bio-recognition binding. We then simulate the charge transport under voltage bias via single-electron tunnelings through the junctions between nanoparticles on such type of networks. We show how the regular nanoparticle array and topologically inhomogeneous nanonetworks affect the charge transport. We find long-range correlations in the time series of charge fluctuation at individual nanoparticles and of flow along the junctions within the network. These correlations explain the occurrence of a large nonlinearity in the simulated and experimentally measured current-voltage characteristics and non-Gaussian fluctuations of the current at the electrode.

cond-mat.soft↗

arXiv2008

Search of Weighted Subgraphs on Complex Networks with Maximum Likelihood Methods

Real-data networks often appear to have strong modularity, or network-of-networks structure, in which subgraphs of various size and consistency occur. Finding the respective subgraph structure is of great importance, in particular for understanding the dynamics on these networks. Here we study modular networks using generalized method of maximum likelihood. We first demonstrate how the method works on computer-generated networks with the subgraphs of controlled connection strengths and clustering. We then implement the algorithm which is based on weights of links and show its efficiency in finding weighted subgraphs on fully connected graph and on real-data network of yeast.

physics.soc-ph↗

arXiv2007

Preferential Behaviour and Scaling in Diffusive Dynamics on Networks

We study the fluctuation properties and return-time statistics on inhomogeneous scale-free networks using packets moving with two different dynamical rules; random diffusion and locally navigated diffusive motion with preferred edges. Scaling in the fluctuations occurs when the dispersion of a quantity at each node or edge increases like the its mean to the power $μ$. We show that the occurrence of scaling in the fluctuations of both the number of packets passing nodes and the number flowing along edges is related to preferential behaviour in either the topology (in the case of nodes) or in the dynamics (in case the of edges). Within our model the absence of any preference leads to the absence of scaling, and when scaling occurs it is non-universal; for random diffusion the number of packets passing a node scales with an exponent $μ$ which increases continuously with increased acquisition time window from $μ=1/2$ at small windows, to $μ=1$ at long time windows; In the preferentially navigated diffusive motion, busy nodes and edges have exponent $μ=1$, in contrast to less busy parts of the network, where an exponent $μ=1/2$ is found. Broad distributions of the return times at nodes and edges illustrate that the basis of the observed scaling is the cooperative behaviour between groups of nodes or edges. These conclusions are relevant for a large class of diffusive dynamics on networks, including packet transport with local navigation rules.

cond-mat.mtrl-sci↗

arXiv2006

Transport on Complex Networks: Flow, Jamming and Optimization

Many transport processes on networks depend crucially on the underlying network geometry, although the exact relationship between the structure of the network and the properties of transport processes remain elusive. In this paper we address this question by using numerical models in which both structure and dynamics are controlled systematically. We consider the traffic of information packets that include driving, searching and queuing. We present the results of extensive simulations on two classes of networks; a correlated cyclic scale-free network and an uncorrelated homogeneous weakly clustered network. By measuring different dynamical variables in the free flow regime we show how the global statistical properties of the transport are related to the temporal fluctuations at individual nodes (the traffic noise) and the links (the traffic flow). We then demonstrate that these two network classes appear as representative topologies for optimal traffic flow in the regimes of low density and high density traffic, respectively. We also determine statistical indicators of the pre-jamming regime on different network geometries and discuss the role of queuing and dynamical betweenness for the traffic congestion. The transition to the jammed traffic regime at a critical posting rate on different network topologies is studied as a phase transition with an appropriate order parameter. We also address several open theoretical problems related to the network dynamics.

physics.soc-ph↗

arXiv2006

Topology of Cell-Aggregated Planar Graphs

We present new algorithm for growth of non-clustered planar graphs by aggregation of cells with given distribution of size and constraint of connectivity k=3 per node. The emergent graph structures are controlled by two parameters--chemical potential of the cell aggregation and the width of the cell size distribution. We compute several statistical properties of these graphs--fractal dimension of the perimeter, distribution of shortest paths between pairs of nodes and topological betweenness of nodes and links. We show how these topological properties depend on the control parameters of the aggregation process and discuss their relevance for the conduction of current in self-assembled nanopatterns.

cond-mat.stat-mech↗

arXiv2006

Transport Processes on Homogeneous Planar Graphs with Scale-Free Loops

We consider the role of network geometry in two types of diffusion processes: transport of constant-density information packets with queuing on nodes, and constant voltage-driven tunneling of electrons. The underlying network is a homogeneous graph with scale-free distribution of loops, which is constrained to a planar geometry and fixed node connectivity $k=3$. We determine properties of noise, flow and return-times statistics for both processes on this graph and relate the observed differences to the microscopic process details. Our main findings are: (i) Through the local interaction between packets queuing at the same node, long-range correlations build up in traffic streams, which are practically absent in the case of electron transport; (ii) Noise fluctuations in the number of packets and in the number of tunnelings recorded at each node appear to obey the scaling laws in two distinct universality classes; (iii) The topological inhomogeneity of betweenness plays the key role in the occurrence of broad distributions of return times and in the dynamic flow. The maximum-flow spanning trees are characteristic for each process type.

cond-mat.stat-mech↗

arXiv2005

Olami-Feder-Christensen Model on different Networks

We investigate numerically the Self Organized Criticality (SOC) properties of the dissipative Olami-Feder-Christensen model on small-world and scale-free networks. We find that the small-world OFC model exhibits self-organized criticality. Indeed, in this case we observe power law behavior of earthquakes size distribution with finite size scaling for the cut-off region. In the scale-free OFC model, instead, the strength of disorder hinders synchronization and does not allow to reach a critical state.

cond-mat.stat-mech↗

arXiv2005

Statistical Indicators of Collective Behavior and Functional Clusters in Gene Networks of Yeast

We analyze gene expression time-series data of yeast S. cerevisiae measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

q-bio.GN↗

arXiv2004

Driving rate effects in avalanche-mediated, first-order phase transitions

We have studied the driving rate and temperature dependence of the power-law exponents that characterize the avalanche distribution in first-order phase transitions. Measurements of acoustic emission in structural transitions in Cu-Zn-Al and Cu-Al-Ni are presented. We show how the observed behaviour emerges within a general framework of competing time scales of avalanche relaxation, driving rate, and thermal fluctuations. We have confirmed our findings by numerical simulations of a prototype model.

cond-mat.dis-nn↗

arXiv2004

Traffic on complex networks: Towards understanding global statistical properties from microscopic density fluctuations

We study the microscopic time fluctuations of traffic-load and the global statistical properties of a dense traffic of particles on scale-free cyclic graphs. For a wide range of driving rates $R$ the traffic is stationary and the load timeseries exhibit anti-persistence due to the regulatory role of the super-structure associated with two hub nodes in the network. We discuss how the super-structure effects the functioning of the network at high traffic density and at the jamming threshold. The degree of correlations systematically decreases with increasing traffic density and eventually disappears when approaching a jamming density $R_c$. Already before jamming we observe qualitative changes in the global network-load distributions and the particle queuing-times. These changes are related to the occurrence of temporary crises in which the network-load increases dramatically, and then slowly falls back to a value characterizing free-flow.

cond-mat.stat-mech↗

arXiv2003

Exploring Complex Graphs by Random Walks

We present an algorithm to grow a graph with scale-free structure of {\it in-} and {\it out-links} and variable wiring diagram in the class of the world-wide Web. We then explore the graph by intentional random walks using local next-near-neighbor search algorithm to navigate through the graph. The topological properties such as betweenness are determined by an ensemble of independent walkers and efficiency of the search is compared on three different graph topologies. In addition we simulate interacting random walks which are created by given rate and navigated in parallel, representing transport with queueing of information packets on the graph.

cond-mat.stat-mech↗

arXiv2003

Information Super-Diffusion on Structured Networks

We study diffusion of information packets on several classes of structured networks. Packets diffuse from a randomly chosen node to a specified destination in the network. As local transport rules we consider random diffusion and an improved local search method. Numerical simulations are performed in the regime of stationary workloads away from the jamming transition. We find that graph topology determines the properties of diffusion in a universal way, which is reflected by power-laws in the transit-time and velocity distributions of packets. With the use of multifractal scaling analysis and arguments of non-extensive statistics we find that these power-laws are compatible with super-diffusive traffic for random diffusion and for improved local search. We are able to quantify the role of network topology on overall transport efficiency. Further, we demonstrate the implications of improved transport rules and discuss the importance of matching (global) topology with (local) transport rules for the optimal function of networks. The presented model should be applicable to a wide range of phenomena ranging from Internet traffic to protein transport along the cytoskeleton in biological cells.

cond-mat.stat-mech↗