Empirical analysis of the evolution of a scientific collaboration network

We present an analysis of the temporal evolution of a scientific coauthorship network, the genetic programming network. We find evidence that the network grows according to preferential attachment, with a slightly sublinear rate. We empirically find how a giant component forms and develops, and we characterize the network by several other time-varying quantities: the mean degree, the clustering coefficient, the average path length, and the degree distribution. We find that the first three statistics increase over time in the growing network; the degree distribution tends to stabilize toward an exponentially truncated power-law. We finally suggest an effective network interpretation that takes into account the aging of collaboration relationships.     

From unweighted to weighted networks with local information

In this paper, we analyze an evolving model with local information which can generate a class of networks by choosing different values of the parameter p. The model introduced exhibits the transition from unweighted networks to weighted networks because the distribution of the edge weight can be widely tuned. With the increase in the local information, the degree correlation of the network transforms from assortative to disassortative. We also study the distribution of the degree, strength and edge weight, which all show crossover between exponential and scale-free. Finally, an application of the proposed model to the study of the synchronization is considered. It is concluded that the synchronizability is enhanced when the heterogeneity of the edge weight is reduced.     

On the spreading and localization of risky information in social networks

We propose a model for the localization of risky information in social (scale free) networks where we assume that risky information can propagate only between “mutually trusted nodes” (MTN). We propose an algorithm to construct the MTN network and show that there is a critical value of trusted nodes below which information localizes. This critical value increases drastically if a fraction p of nodes does not transfer information at all. We study the fraction of initial messengers needed to inform a desired fraction of the network as a function of the average number of trusted nodes and discuss possible applications of the model also to marketing and to the spreading of a disease with very short incubation time.     

Community structure in the United States House of Representatives

We investigate the networks of committee and subcommittee assignments in the United States House of Representatives from the 101st-108th Congresses, with the committees connected by “interlocks” or common membership. We examine the community structure in these networks using several methods, revealing strong links between certain committees as well as an intrinsic hierarchical structure in the House as a whole. We identify structural changes, including additional hierarchical levels and higher modularity, resulting from the 1994 election, in which the Republican party earned majority status in the House for the first time in more than 40 years. We also combine our network approach with the analysis of roll call votes using singular value decomposition to uncover correlations between the political and organizational structure of House committees.     

Cumulative Laws, Team Assembling Mechanisms Determining Network Structure

A number of researching works have shed light on the field of complex networks recently. We investigate a wide range of real-world networks and find several interesting phenomena. Firstly, almost all of these networks evolve by overlapping new small graphs on former networks. Secondly, not only the degree sequence of the mature network follows a power-law distribution, but also the distribution of the cumulative occurrence times during the growing process are revealed to have a heavy tail. Existing network evolving models do not provide interpretation to these phenomena. We suggest a model based on the team assembling mechanism, which is extracted from the growing processes of real-world networks and requires simple parameters, and produces networks exhibiting these properties observed in the present study and in previous works.     

Multi-directed Eulerian growing networks

We introduce and analyze a model of a multi-directed Eulerian network, that is a directed and weighted network where a path exists that passes through all the edges of the network once and only once. Networks of this type can be used to describe information networks such as human language or DNA chains. We are able to calculate the strength and degree distribution in this network and find that they both exhibit a power law with an exponent between 2 and 3. We then analyze the behavior of the accelerated version of the model and find that the strength distribution has a double slope power-law behavior. Finally we introduce a non-Eulerian version of the model and find that the statistical topological properties remain unchanged. Our analytical results are compared with numerical simulations.     

The network of scientific collaborations within the European framework programme

We use the emergent field of complex networks to analyze the network of scientific collaborations between entities (universities, research organizations, industry related companies,…) which collaborate in the context of the so-called framework programme. We demonstrate here that it is a scale-free network with an accelerated growth, which implies that the creation of new collaborations is encouraged. Moreover, these collaborations possess hierarchical modularity. Likewise, we find that the information flow depends on the size of the participants but not on geographical constraints.     

Ensemble inequivalence in random graphs

We present a complete analytical solution of a system of Potts spins on a random k-regular graph in both the canonical and microcanonical ensembles, using the Large Deviation Cavity Method (LDCM). The solution is shown to be composed of three different branches, resulting in a non-concave entropy function. The analytical solution is confirmed with numerical Metropolis and Creutz simulations and our results clearly demonstrate the presence of a region with negative specific heat and, consequently, ensemble inequivalence between the canonical and microcanonical ensembles.     

Immunization of susceptible-infected model on scale-free networks

In this paper, we investigate two major immunization strategies, random immunization and targeted immunization, of the susceptible-infected (SI) model on the Barabási-Albert (BA) networks. For the heterogeneous structure, the random strategy is quite ineffective if the vaccinated proportion is small, while the targeted one which prefers to vaccinate the individuals with the largest degree can sharply depress the epidemic spreading even only a tiny fraction of population are vaccinated. The analytical solution is also obtained, which can capture the trend of velocity change vs. the amount of vaccinated population.     

A new deterministic complex network model with hierarchical structure

We introduce a new simple pseudo tree-like network model, deterministic complex network (DCN). The proposed DCN model may simulate the hierarchical structure nature of real networks appropriately and have the unique property of ‘skipping the levels’, which is ubiquitous in social networks. Our results indicate that the DCN model has a rather small average path length and large clustering coefficient, leading to the small-world effect. Strikingly, our DCN model obeys a discrete power-law degree distribution P(k)∝k^−γ, with exponent γ approaching 1.0. We also discover that the relationship between the clustering coefficient and degree follows the scaling law C(k)∼k⁻¹, which quantitatively determines the DCN's hierarchical structure.     

Topological Self-Similar Networks Introduced by Diffusion-Limited Aggregation Mechanism

We propose a model for growing fractal networks based on the mechanisms learned from the diffusion-limited aggregation （DLA） model in fractal geometries in the viewpoint of network. By studying the DLA network, our model introduces multiplicative growth, aging and geographical preferential attachment mechanisms, whereby featuring topological self-similar property and hierarchical modularity. According to the results of theoretical analysis and simulation, the degree distribution of the proposed model shows a mixed degree distribution （i.e., exponential and algebraic degree distribution） and the fractal dimension and clustering coefficient can be tuned by changing the values of parameters.     

Modeling subspecies and the tumor-immune system interaction: Steps toward understanding therapy

A mesoscopic nutrient competition model for cancer growth is generalized to describe the growth of a heterogeneous tumor and the interactions between the tumor and the immune system. Our simulations show that the success of a mutation depends not only on its intrinsic competitive advantages, but also on its location in the tumor mass. It is also shown that the simple killing of tumor cells by immune cells, even when their activity is increased by therapy, is not sufficient to stem tumor growth, but another mechanism (such as pinning) is needed for a successful therapy.     

Model for UV induced formation of gold nanoparticles in solid polymeric matrices

UV irradiation of polymeric PMMA films containing HAuCl₄ followed by annealing at 60-80 °C forms gold nanoparticles directly within the bulk material. The kinetics of nanoparticle formation was traced by extinction spectra of nanocomposite film changes vs annealing time. We propose that UV irradiation causes HAuCl₄ dissociation and thus provides a polymeric matrix with atomic gold. The presence of an oversaturated solid solution of atomic gold in the polymeric matrix leads to Au nanoparticle formation during annealing. This process can be understood as a phase transition of the first order. In this paper we apply several common kinetic models of the phase transition for describing Au nanoparticle formation inside the solid polymer matrix. We compare predictions of these models with the experimental data and show that these models cannot describe the process. We propose that the stabilization effect of the matrix on the growing gold nanoparticles is important. The simplest model introducing some probability for the transition from growing nanoparticle to the non-growing, stabilized form is suggested. It is shown that this model satisfactorily describes the experimentally observed evolution of the extinction spectrum of Au nanoparticles forming in a polymer matrix.     

Networks of interactions in the secondary and tertiary structure of ribosomal RNA

We construct four different structural networks for both the secondary and tertiary structures of the 16S and 23S ribosomal RNAs (rRNAs) in the high-resolution crystal structures of the Thermus thermophilus 30S and Haloarcula marismortui 50S ribosomal subunits, and investigate topological characteristics of the rRNA structures by determining relevant measures, such as the characteristic path length, the clustering coefficient, and the helix betweenness. This study reveals that the 23S rRNA network is more compact than the 16S rRNA networks, reflecting the more globular overall structure of the 23S rRNA relative to the 16S rRNA. In particular, the large number of tertiary interactions in the 23S rRNA tends to cluster, accounting for its small-world network properties. In addition, although the rRNA networks are not the scale-free network, their helix betweenness has a power-law distribution and is correlated with the phylogenetic conservation of helices. The higher the helix betweenness, the more conserved the helix. These results suggest a potential role of the rRNA network as a new quantitative approach in rRNA research.     

The emergence of scale-free networks with a seceding mechanism

In order to explore further the underlying mechanism of scale-free networks, we study stochastic secession as a mechanism for the creation of complex networks. In this evolution the network growth incorporates the addition of new nodes, the addition of new links between existing nodes, the deleting and rewiring of some existing links, and the stochastic secession of nodes. To random growing networks with preferential attachment, the model yields scale-free behavior for the degree distribution. Furthermore, we obtain an analytical expression of the power-law degree distribution with scaling exponent γ ranging from 1.1 to 9. The analytical expressions are in good agreement with the numerical simulation results.     

A very fast algorithm for detecting community structures in complex networks

In this paper, a new algorithm is proposed, which uses only local information to analyze community structures in complex networks. The algorithm is based on a table that describes a network and a virtual cache similar to the cache in the computer structure. When being tested on some typical computer-generated and real-world networks, this algorithm demonstrates excellent detection results and very fast processing performance, much faster than the existing comparable algorithms of the same kind.     

Local affinity in heterogeneous growing networks

In this paper we present a study of the influence of local affinity in heterogeneous preferential attachment (PA) networks. Heterogeneous PA models are a generalization of the Barabási-Albert model to heterogeneous networks, where the affinity between nodes biases the attachment probability of links. Threshold models are a class of heterogeneous PA models where the affinity between nodes is inversely related to the distance between their states. We propose a generalization of threshold models where network nodes have individual affinity functions, which are then combined to yield the affinity of each potential interaction. We analyze the influence of the affinity functions in the topological properties averaged over a network ensemble. The network topology is evaluated through the distributions of connectivity degrees, clustering coefficients and geodesic distances. We show that the relaxation of the criterion of a single global affinity still leads to a reasonable power-law scaling in the connectivity and clustering distributions under a wide spectrum of assumptions. We also show that the richer behavior of the model often exhibits a better agreement with the empirical observations on real networks.     

A model dielectric response function for metallic nanotube ropes

We propose a model dielectric function for ropes of single-walled nanotubes distributed in a glassy graphite host medium. We study the significance of the bosonic charge excitations arising in interacting quasi-one-dimensional systems in the screening processes. We also pay special attention to the role of the intertube Coulomb interactions. In order to compare with experiments, weak relaxation processes are also considered in the relaxation-time approximation.     

Entropy of electromyography time series

A nonlinear analysis based on Renyi entropy is applied to electromyography (EMG) time series from back muscles. The time dependence of the entropy of the EMG signal exhibits a crossover from a subdiffusive regime at short times to a plateau at longer times. We argue that this behavior characterizes complex biological systems. The plateau value of the entropy can be used to differentiate between healthy and low back pain individuals.     

Tail universalities in rank distributions as an algebraic problem: The beta-like function

Although power laws of the Zipf type have been used by many workers to fit rank distributions in different fields like in economy, geophysics, genetics, soft-matter, networks, etc. these fits usually fail at the tail. Some distributions have been proposed to solve the problem, but unfortunately they do not fit at the same time the body and the tail of the distribution. We show that many different data in rank laws, like in granular materials, codons, author impact in scientific journal, etc. can be very well fitted by the integrand of a beta function (that we call beta-like function). Then we propose that such universality can be due to the fact that systems made from many subsystems or choices, present stretched exponential frequency-rank functions which qualitatively and quantitatively are well fitted with the beta-like function distribution in the limit of many random variables. We give a plausibility argument for this observation by transforming the problem into an algebraic one: finding the rank of successive products of numbers, which is basically a multinomial process. From a physical point of view, the observed behavior at the tail seems to be related with the onset of different mechanisms that are dominant at different scales, providing crossovers and finite size effects.