arXiv subjects

Ljupco Kocarev

Publications and source records attributed to Ljupco Kocarev.

At least 55 records · Page 3Linked to original sources

arXiv2016

Bridging Online and Offline Social Networks: Multiplex Analysis

We show that three basic actor characteristics, namely normalized reciprocity, three cycles, and triplets, can be expressed using an unified framework that is based on computing the similarity index between two sets associated with the actor: the set of her/his friends and the set of those considering her/him as a friend. These metrics are extended to multiplex networks and then computed for two friendship networks generated by collecting data from two groups of undergraduate students. We found that in offline communication strong and weak ties are (almost) equally presented, while in online communication weak ties are dominant. Moreover, weak ties are much less reciprocal than strong ties. However, across different layers of the multiplex network reciprocities are preserved, while triads (measured with normalized three cycles and triplets) are not significant.

cs.SI↗

arXiv2016

Graphlet characteristics in directed networks

A number of network structural characteristics have recently been the subject of particularly intense research, including degree distributions, community structure, and various measures of vertex centrality, to mention only a few. Vertices may have attributes associated with them; for example, properties of proteins in protein-protein interaction networks, users' social network profiles, or authors' publication histories in co-authorship networks. In a network, two vertices might be considered similar if they have similar attributes (features, properties), or they can be considered similar based solely on the network structure. Similarity of this type is called structural similarity, to distinguish it from properties similarity, social similarity, textual similarity, functional similarity or other similarity types found in networks. Here we focus on the similarity problem by computing (1) for each vertex a vector of structural features, called signature vector, based on the number of graphlets associated with the vertex, and (2) for the network its graphlet correlation matrix, measuring graphlets dependencies and hence revealing unknown organizational principles of the network. We found that real-world networks generally have very different structural characteristics resulting in different graphlet correlation matrices. In particular, the graphlet correlation matrix of the brain effective network is computed for 40 healthy subjects and common (present in more than 70 percent subjects) dependencies are raveled. Thus, negative correlations are found for 2-node graphlets and 3-node graphlets that are wedges and positive correlations are found only for 3-node graphlets that are triangles. Graphlets characteristics in directed networks could further significantly increase our understanding of real-world networks.

cs.SI↗

arXiv2016

Analysis of renewable energy sources and electric vehicle penetration into energy systems predominantly based on lignite

We consider an integration of renewable energy into transport and electricity sectors through vehicle to grid (V2G) technologies for an energy system that is predominantly based on lignite. The national energy system of Macedonia is modeled using EnergyPLAN which integrates energy for electricity, transport and heat, and includes hourly fluctuations in human needs and the environment. We show that electric-vehicles can provide the necessary storage enabling a fully renewable energy profile for Macedonia that can match the country's growing demand for energy. Furthermore, a large penetration of electric vehicles leads to a dramatic reduction of 47% of small particles and other air pollutants generated by car traffic in 2050.

physics.soc-ph↗

arXiv2016

Greedy reduction of navigation time in random search processes

Random search processes are instrumental in studying and understanding navigation properties of complex networks, food search strategies of animals, diffusion control of molecular processes in biological cells, and improving web search engines. An essential part of random search processes and their applications are various forms of (continuous or discrete time) random walk models. The efficiency of a random search strategy in complex networks is measured with the mean first passage time between two nodes or, more generally, with the mean first passage time between two subsets of the vertex set. In this paper we formulate a problem of adding a set of $k$ links between the two subsets of the vertex set that optimally reduce the mean first passage time between the sets. We demonstrate that the mean first passage time between two sets is non-increasing and supermodular set function defined over the set of links between the two sets. This allows us to use two greedy algorithms that approximately solve the problem and we compare their performance against several standard link prediction algorithms. We find that the proposed greedy algorithms are better at choosing the links that reduce the navigation time between the two sets.

cs.SI↗

arXiv2015

A Strategy for Power System Stability Improvement via Controlled Charge/Discharge of Plug-in Electric Vehicles

Plug-in electrical vehicles (PEV) are capable of both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) power transfer. The advantages of developing V2G include an additional revenue stream for cleaner vehicles, increased stability and reliability of the electric grid, lower electric system costs, and eventually, inexpensive storage and backup for renewable electricity. Here we show how smart control of PEVs can improve the stability of power grids using only local frequency measurements. We evaluate the proposed control strategy on the IEEE Case 3 and the IEEE New England power systems. The results show that V2G leads to improved steady-state stability, larger region of stability, reduced frequency and voltage fluctuations during transients and longer critical clearing times.

eess.SY↗

arXiv2014

Opinion mining of text documents written in Macedonian language

The ability to extract public opinion from web portals such as review sites, social networks and blogs will enable companies and individuals to form a view, an attitude and make decisions without having to do lengthy and costly researches and surveys. In this paper machine learning techniques are used for determining the polarity of forum posts on kajgana which are written in Macedonian language. The posts are classified as being positive, negative or neutral. We test different feature metrics and classifiers and provide detailed evaluation of their participation in improving the overall performance on a manually generated dataset. By achieving 92% accuracy, we show that the performance of systems for automated opinion mining is comparable to a human evaluator, thus making it a viable option for text data analysis. Finally, we present a few statistics derived from the forum posts using the developed system.

cs.CL↗

arXiv2014

Improved steady-state stability of power grids with a communication infrastructure

Efficient control of power systems is becoming increasingly difficult as they gain in complexity and size. We propose an automatic control strategy that regulates the mechanical power output of the generators in a power grid based on information obtained via a communication infrastructure. An algorithm that optimizes steady-state stability of a power grid by iteratively adding communication links is presented. The proposed control scheme is successfully applied to the IEEE New England and IEEE RTS 96 power systems, leading to a significant increase in the steady-state stability of the systems and an improvement in their overall robustness. The resulting communication network topology differs significantly from the transmission grid topology. This shows how complex the steady- state control for power systems is, influenced by the generators configuration, the transmission network topology, and the manner by which control is executed.

eess.SY↗

arXiv2014

Improving power grid transient stability by plug-in electric vehicles

Plug-in electric vehicles (PEVs) can serve in discharge mode as distributed energy and power resources operating as vehicle-to-grid (V2G) devices and in charge mode as loads or grid-to-vehicle (G2V) devices. It has been documented that PEVs serving as V2G systems can offer possible backup for renewable power sources, can provide reactive power support, active power regulation, load balancing, peak load shaving,% and current harmonic filtering, can provide ancillary services as frequency control and spinning reserves, can improve grid efficiency, stability, reliability, and generation dispatch, can reduce utility operating costs and can generate revenue. Here we show that PEVs can even improve power grid transient stability, that is, stability when the power grid is subjected to large disturbances, including bus faults, generator and branch tripping, and sudden large load changes. A control strategy that regulates the power output of a fleet of PEVs based on the speed of generator turbines is proposed and tested on the New England 10-unit 39-bus power system. By regulating the power output of the PEVs we show that (1) speed and voltage fluctuations resulting from large disturbances can be significantly reduced up to 5 times, and (2) the critical clearing time can be extended by 20-40%. Overall, the PEVs control strategy makes the power grid more robust.

eess.SY↗

arXiv2014

On Energy-efficiency in Wireless Networks: A Game-theoretic Approach to Cooperation Inspired by Evolutionary Biology

We develop a game-theoretic framework to investigate the effect of cooperation on the energy efficiency in wireless networks. We address two examples of network architectures, resembling ad-hoc network and network with central infrastructure node. Most present approaches address the issue of energy efficiency in communication networks by using complex algorithms to enforce cooperation in the network, followed by extensive signal processing at the network nodes. Instead, we address cooperative communication scenarios which are governed by simple, evolutionary-like, local rules, and do not require strategic complexity of the network nodes. The approach is motivated by recent results in evolutionary biology which suggest that cooperation can emerge in Nature by evolution, i. e. can be favoured by natural selection, if certain mechanism is at work. As result, we are able to show by experiments that cooperative behavior can indeed emerge and persist in wireless networks, even if the behavior of the individual nodes is driven by selfish decision making. The results from this work indicate that uncomplicated local rules, followed by simple fitness evaluation, can promote cooperation and generate network behavior which yields global energy efficiency in certain wireless networks.

cs.NI↗

arXiv2014

Energy-efficiency in Decentralized Wireless Networks: A Game-theoretic Approach inspired by Evolutionary Biology

Energy efficiency is gaining importance in wireless communication networks which have nodes with limited energy supply and signal processing capabilities. We present a numerical study of cooperative communication scenarios based on simple local rules. This is in contrast to most of the approaches in the literature which enforce cooperation by using complex algorithms and require strategic complexity of the network nodes. The approach is motivated by recent results in evolutionary biology which suggest that, if certain mechanism is at work, cooperation can be favoured by natural selection, i. e. even selfish actions of the individual nodes can lead to emergence of cooperative behaviour in the network. The results of the simulations in the context of wireless communication networks verify these observations and indicate that uncomplicated local rules, followed by simple fitness evaluation, can generate network behaviour which yields global energy efficiency.

cs.NI↗

arXiv2014

Beyond network structure: How heterogenous susceptibility modulates the spread of epidemics

The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that--for the SIS model--differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.

physics.soc-ph↗

arXiv2011

Identifying communities by influence dynamics in social networks

Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algorithm for community detection should not only quantify the topology of the network, but incorporate the dynamical processes that take place on the network. We present a novel algorithm for community detection that combines network structure with processes that support creation and/or evolution of communities. The algorithm does not embrace the universal approach but instead tries to focus on social networks and model dynamic social interactions that occur on those networks. It identifies leaders, and communities that form around those leaders. It naturally supports overlapping communities by associating each node with a membership vector that describes node's involvement in each community. This way, in addition to overlapping communities, we can identify nodes that are good followers to their leader, and also nodes with no clear community involvement that serve as a proxy between several communities and are equally as important. We run the algorithm for several real social networks which we believe represent a good fraction of the wide body of social networks and discuss the results including other possible applications.

physics.soc-ph↗

arXiv2011

The influence of the network topology on epidemic spreading

The influence of the network's structure on the dynamics of spreading processes has been extensively studied in the last decade. Important results that partially answer this question show a weak connection between the macroscopic behavior of these processes and specific structural properties in the network, such as the largest eigenvalue of a topology related matrix. However, little is known about the direct influence of the network topology on microscopic level, such as the influence of the (neighboring) network on the probability of a particular node's infection. To answer this question, we derive both an upper and a lower bound for the probability that a particular node is infective in a susceptible-infective-susceptible model for two cases of spreading processes: reactive and contact processes. The bounds are derived by considering the $n-$hop neighborhood of the node; the bounds are tighter as one uses a larger $n-$hop neighborhood to calculate them. Consequently, using local information for different neighborhood sizes, we assess the extent to which the topology influences the spreading process, thus providing also a strong macroscopic connection between the former and the latter. Our findings are complemented by numerical results for a real-world e-mail network. A very good estimate for the infection density $ρ$ is obtained using only 2-hop neighborhoods which account for 0.4% of the entire network topology on average.

physics.soc-ph↗

arXiv2011

Analytically solvable processes on networks

We introduce a broad class of analytically solvable processes on networks. In the special case, they reduce to random walk and consensus process - two most basic processes on networks. Our class differs from previous models of interactions (such as stochastic Ising model, cellular automata, infinite particle system, and voter model) in several ways, two most important being: (i) the model is analytically solvable even when the dynamical equation for each node may be different and the network may have an arbitrary finite graph and influence structure; and (ii) in addition, when local dynamic is described by the same evolution equation, the model is decomposable: the equilibrium behavior of the system can be expressed as an explicit function of network topology and node dynamics

physics.soc-ph↗

arXiv2011

Rich-club and page-club coefficients for directed graphs

Rich-club and page-club coefficients and their null models are introduced for directed graphs. Null models allow for a quantitative discussion of the rich-club and page-club phenomena. These coefficients are computed for four directed real-world networks: Arxiv High Energy Physics paper citation network, Web network (released from Google), Citation network among US Patents, and Email network from a EU research institution. The results show a high correlation between rich-club and page-club ordering. For journal paper citation network, we identify both rich-club and page-club ordering, showing that {}"elite" papers are cited by other {}"elite" papers. Google web network shows partial rich-club and page-club ordering up to some point and then a narrow declining of the corresponding normalized coefficients, indicating the lack of rich-club ordering and the lack of page-club ordering, i.e. high in-degree (PageRank) pages purposely avoid sharing links with other high in-degree (PageRank) pages. For UC patents citation network, we identify page-club and rich-club ordering providing a conclusion that {}"elite" patents are cited by other {}"elite" patents. Finally, for e-mail communication network we show lack of both rich-club and page-club ordering. We construct an example of synthetic network showing page-club ordering and the lack of rich-club ordering.

physics.soc-ph↗

arXiv2009

Random walks on networks: cumulative distribution of cover time

We derive an exact closed-form analytical expression for the distribution of the cover time for a random walk over an arbitrary graph. In special case, we derive simplified exact expressions for the distributions of cover time for a complete graph, a cycle graph, and a path graph. An accurate approximation for the cover time distribution, with computational complexity of O(2n), is also presented. The approximation is numerically tested only for graphs with n<=1000 nodes.

math-ph↗

arXiv2007

Distribution of Edge Load in Scale-free Trees

Node betweenness has been studied recently by a number of authors, but until now less attention has been paid to edge betweenness. In this paper, we present an exact analytic study of edge betweenness in evolving scale-free and non-scale-free trees. We aim at the probability distribution of edge betweenness under the condition that a local property, the in-degree of the ``younger'' node of a randomly selected edge, is known. En route to the conditional distribution of edge betweenness the exact joint distribution of cluster size and in-degree, and its one dimensional marginal distributions have been presented in the paper as well. From the derived probability distributions the expectation values of different quantities have been calculated. Our results provide an exact solution not only for infinite, but for finite networks as well.

cs.NI↗

arXiv2005

The permutation entropy rate equals the metric entropy rate for ergodic information sources and ergodic dynamical systems

Permutation entropy quantifies the diversity of possible orderings of the values a random or deterministic system can take, as Shannon entropy quantifies the diversity of values. We show that the metric and permutation entropy rates--measures of new disorder per new observed value--are equal for ergodic finite-alphabet information sources (discrete-time stationary stochastic processes). With this result, we then prove that the same holds for deterministic dynamical systems defined by ergodic maps on $n$% -dimensional intervals. This result generalizes a previous one for piecewise monotone interval maps on the real line (Bandt, Keller and Pompe, "Entropy of interval maps via permutations",\textit{Nonlinearity} \textbf{15}, 1595-602, (2002)), at the expense of requiring ergodicity and using a definition of permutation entropy rate differing in the order of two limits. The case of non-ergodic finite-alphabet sources is also studied and an inequality developed. Finally, the equality of permutation and metric entropy rates is extended to ergodic non-discrete information sources when entropy is replaced by differential entropy in the usual way.

nlin.CD↗