Finding communities in weighted networks through synchronization

Community detection in weighted networks is an important challenge. In this paper, we introduce a local weight ratio scheme for identifying the community structures of weighted networks within the context of the Kuramoto model by taking into account weights of links. The proposed scheme takes full advantage of the information of the link density among vertices and the closeness of relations between each vertex and its neighbors. By means of this scheme, we explore the connection between community structures and dynamic time scales of synchronization. Moreover, we can also unravel the hierarchical structures of weighted networks with a well-defined connectivity pattern by the synchronization process. The performance of the proposed method is evaluated on both computer-generated benchmark graphs and real-world networks.     

On the structure of unitary conformal field theory II: Representation theoretic approach

Two-dimensional, unitary rational conformal field theory is studied from the point of view of the representation theory of chiral algebras. Chiral algebras are equipped with a family of co-multiplications which serve to define tensor product representations. Chiral vertices arise as Clebsch-Gordan operators from tensor product representations to irreducible subrepresentations of a chiral algebra. The algebra of chiral vertices is studied and shown to give rise to representations of the braid groups determined by Yang-Baxter (braid) matrices. Chiral fusion is analyzed. It is shown that the braid- and fusion matrices determine invariants of knots and links. Connections between the representation theories of chiral algebras and of quantum groups are sketched. Finally, it is shown how the local fields of a conformal field theory can be reconstructed from the chiral vertices of two chiral algebras.     

Network formed by traces of random walks

We propose a model of time evolving networks in which a kind of transport between vertices generates new edges in the graph. We call the model “Network formed by traces of random walks”, because the transports are represented abstractly by random walks. Our numerical calculations yield several important properties observed commonly in complex networks, although the graph at initial time is only a one-dimensional lattice. For example, the distribution of vertex degree exhibits various behaviors such as exponential, power law like, and bi-modal distribution according to change of probability of extinction of edges. Another property such as strong clustering structure and small mean vertex–vertex distance can also be found. The transports represented by random walks in a framework of strong links between regular lattice is a new mechanisms which yields biased acquisition of links for vertices.     

Correlated random networks

We develop a statistical theory of networks. A network is a set of vertices and links given by its adjacency matrix c, and the relevant statistical ensembles are defined in terms of a partition function Z= summation operator exp([-betaH(c)]. The simplest cases are uncorrelated random networks such as the well-known Erd?s-Rényi graphs. Here we study more general interactions H(c) which lead to correlations, for example, between the connectivities of adjacent vertices. In particular, such correlations occur in optimized networks described by partition functions in the limit beta--> infinity. They are argued to be a crucial signature of evolutionary design in biological networks.     

On the description of transport phenomena

The linear response of a quantity like the electric current to a time and space dependent field can be described by Kubo's formula, and this again can be written as a resolvent. The expansion of this resolvent yields exactly a transport equation of the structure of the Boltzmann equation. Perturbation theory — the only practical way to deal with it — gives back the usual Boltzmann equation. This derivation has advantages like yielding a symmetric collision operator. Also including time and space dependence with this approach is very easy. The purpose of this paper is to show the closeness of linear response theory and kinetic equations. We also discuss merits and drawbacks of the resolvent method.     

Detecting hierarchical and overlapping network communities using locally optimal modularity changes

Agglomerative clustering is a well established strategy for identifying communities in networks. Communities are successively merged into larger communities, coarsening a network of actors into a more manageable network of communities. The order in which merges should occur is not in general clear, necessitating heuristics for selecting pairs of communities to merge. We describe a hierarchical clustering algorithm based on a local optimality property. For each edge in the network, we associate the modularity change for merging the communities it links. For each community vertex, we call the preferred edge that edge for which the modularity change is maximal. When an edge is preferred by both vertices that it links, it appears to be the optimal choice from the local viewpoint. We use the locally optimal edges to define the algorithm: simultaneously merge all pairs of communities that are connected by locally optimal edges that would increase the modularity, redetermining the locally optimal edges after each step and continuing so long as the modularity can be further increased. We apply the algorithm to model and empirical networks, demonstrating that it can efficiently produce high-quality community solutions. We relate the performance and implementation details to the structure of the resulting community hierarchies. We additionally consider a complementary local clustering algorithm, describing how to identify overlapping communities based on the local optimality condition.     

Ranking spreaders by decomposing complex networks

Ranking the nodes? ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.     

Dual Feynman rules—Topological asymptotic freedom

Feynman-graph rules are formulated for the strong—interaction components of the topological expansion—defined as those graphs all of whose vertices are zero—entropy connected parts. These rules imply a “topological asymptotic freedom” and admit a corresponding perturbative evaluation where the zeroth order exhibits topological supersymmetry.     

Evolving model of weighted networks inspired by scientific collaboration networks

Inspired by scientific collaboration networks (SCN), especially our empirical analysis of econophysicists network, an evolutionary model for weighted networks is proposed. Besides a new vertex added in at every time step, old vertices can also attempt to build up new links, or to reconnect the existing links. The number of connections repeated between two nodes is converted into the weight of the link. This provides a natural way for the evolution of link weight. The path-dependent preferential attachment mechanism with local information is also introduced. It increases the clustering coefficient of the network significantly. The model shows the scale-free phenomena in degree and vertex weight distribution. It also gives well qualitatively consistent behavior with the empirical results.     

The calculation of singular points in the supercritical region for a system with a Lennard—Jones interaction potential

In this work the positions of the critical point, the supercritical point, and the maximum fluctuation point in a supercritical isotherm were found for a system with the Lennard—Jones interaction potential. Virial coefficients and methods based on accelerated convergence of the perturbation-theory series, which are well known for such systems, were used. The results were compared with computer-simulation data. As has been established, if one uses the positively defined Weeks—Chandler—Andersen potential as a reference system, the calculated parameters tend monotonically to exact values as a function of the number of virial coefficients. This decomposition is favorably different from the virial one, where the aspiration is not monotonic. These results indicate that this method makes it possible to determine the positions of the three vertices of the supercritical triangle with an accuracy that is comparable to that of a simulated experiment.     

A box-covering algorithm for fractal scaling in scale-free networks

A random sequential box-covering algorithm recently introduced to measure the fractal dimension in scale-free (SF) networks is investigated. The algorithm contains Monte Carlo sequential steps of choosing the position of the center of each box; thereby, vertices in preassigned boxes can divide subsequent boxes into more than one piece, but divided boxes are counted once. We find that such box-split allowance in the algorithm is a crucial ingredient necessary to obtain the fractal scaling for fractal networks; however, it is inessential for regular lattice and conventional fractal objects embedded in the Euclidean space. Next, the algorithm is viewed from the cluster-growing perspective that boxes are allowed to overlap; thereby, vertices can belong to more than one box. The number of distinct boxes a vertex belongs to is, then, distributed in a heterogeneous manner for SF fractal networks, while it is of Poisson-type for the conventional fractal objects.     

Scale-free networks from varying vertex intrinsic fitness

A new mechanism leading to scale-free networks is proposed in this Letter. It is shown that, in many cases of interest, the connectivity power-law behavior is neither related to dynamical properties nor to preferential attachment. Assigning a quenched fitness value x(i) to every vertex, and drawing links among vertices with a probability depending on the fitnesses of the two involved sites, gives rise to what we call a good-get-richer mechanism, in which sites with larger fitness are more likely to become hubs (i.e., to be highly connected).     

Modelling local voids using an irregular polyhedron based on natural neighbourhood and application to characterize near-dense random packing (DRP)

A model has been developed for finding local voids in randomly packed monodisperse spheres. The voids are polyhedral in shape and are based on the natural neighbourhood concept. The natural neighbourhood is defined in the same spirit of Sibson, who introduced the concept as a refinement of Voronoi tessellation. The model is basically the construction of a Delaunay star, where the centre of the Delaunay star is an arbitrary point in the void and the vertices of the star are the sphere centres. The method is best suited for sampling study. Since the model does not use the radius of the spheres, it can even be used for point distribution in three-dimensional (3-D) space. The model can be improved by using Voronoi vertices as seed points (instead of the arbitrary points) and can be used for crystallochemical studies, where only the electron density distribution is known. It is applicable to non-spherical atoms/particles also. The method is used to analyze near-dense random packing (DRP) and the statistical properties of void structures, e.g. number of vertices per void, cell volume, void volume and void fraction, which do not change from packing to packing in the limit of DRP. The overall local void properties are insensitive to sampling; repeatedly taking 500 void samples in an ensemble did not show considerable change. Most of the voids have 9–12 vertices.     

Local Events and Dynamics on Weighted Complex Networks

We examine the weighted networks grown and evolved by local events, such as the addition of new vertices and links and we show that depending on frequency of the events, a generalized power-law distribution of strength can emerge. Continuum theory is used to predict the scaling function as well as the exponents, which is in good agreement with the numerical simulation results. Depending on event frequency, power-law distributions of degree and weight can also be expected. Probability saturation phenomena for small strength and degree in many real world networks can be reproduced. Particularly, the non-trivial clustering coefficient, assortativity coefficient and degree-strength correlation in our model are all consistent with empirical evidences.     

Dominating sets and ego–centered decompositions in social networks

Our aim here is to address the problem of decomposing a whole network into a minimal number of ego–centered subnetworks. For this purpose, the network egos are picked out as the members of a minimum dominating set of the network. However, to find such an efficient dominating ego–centered construction, we need to be able to detect all the minimum dominating sets and to compare all the corresponding dominating ego–centered decompositions of the network. To find all the minimum dominating sets of the network, we are developing a computational heuristic, which is based on the partition of the set of nodes of a graph into three subsets, the always dominant vertices, the possible dominant vertices and the never dominant vertices, when the domination number of the network is known. To compare the ensuing dominating ego–centered decompositions of the network, we are introducing a number of structural measures that count the number of nodes and links inside and across the ego–centered subnetworks. Furthermore, we are applying the techniques of graph domination and ego–centered decomposition for six empirical social networks.     

Pathlength scaling in graphs with incomplete navigational information

The graph-navigability problem concerns how one can find as short paths as possible between a pair of vertices, given an incomplete picture of a graph. We study the navigability of graphs where the vertices are tagged by a number (between 1 and the total number of vertices) in a way to aid navigation. This information is too little to ensure errorfree navigation but enough, as we will show, for the agents to do significantly better than a random walk. In our setup, given a graph, we first assign information to the vertices that agents can utilize for their navigation. To evaluate the navigation, we calculate the average distance traveled over random pairs of source and target and different graph realizations. We show that this type of embedding can be made quite efficiently; the more information is embedded, the more efficient it gets. We also investigate the embedded navigational information in a standard graph layout algorithm and find that although this information does not make algorithms as efficient as the above-mentioned schemes, it is significantly helpful.     

THEORY OF RAMAN SCATTERING FROM CHARGE DENSITY-WAVE SUPERCONDUCTORS

A theory is developed for the Raman scattering of light from a charge-density-wave (CDW) superconductor on the basis of a modified Balseiro-Falicov interactibn pro.posed by the authors and including renormalization of both the Coulomb interaction at the small q limit, and the residual coupling between electrons. Both the electron-photon and electron-phonon vertices are taken into account. It is shown that there always exist poles at frequency ω=2Δ (Δ is superconducting gap) in the effective electron polarization and in the phonon self-energy, and these poles survive the Coulomb screening and the renormalization of the residual electron interactions if the coupling parameter g₂(k) is anisotropic, in contrast with an isotropic electron gas. The effect of the Littlewood-Varma interaction in a coexistent CDW-siiperconductcr is also discussed.     

Quasi-steady processes of a high current Z-pinch

Quasi-steady states of an isolated high current Z-pinch are investigated analytically using two-temperature equations averaged in space. Some characteristic processes — a steady compression process etc. — are given. A new explanation for the results of the laser initiated gas-embedded Z-pinch experiments is also given.     

An Experimental Study for Enhanced Availability of Free-Space Optics Systems over Hybrid Free-Space Optics/Radio Frequency Systems Using Switching Scheme Based on On–Off Keying and Multiple Pulse Position Modulation Schemes with Variable Data Rates

Abstract

Free-space optic links are degraded by such weather conditions as rain, fog, and atmospheric scintillation. The inherent advantages of free-space optic communication—providing higher data rates, security, and easy redeployment ability—motivate the avoidance of switching over free-space optic links to low bandwidth radio frequency links. This article establishes viable alternatives over free-space optics/radio frequency hybrid links to achieve higher capacity operation exploiting on–off keying and multiple pulse position modulation schemes with variable data rates to overcome the effect of fading due to adverse weather conditions. Results of an experimental free-space optical link at 1,550 nm are presented.