Cut-offs and finite size effects in scale-free networks

We analyze the degree distributions cut-off in finite size scale-free networks. We show that the cut-off behavior with the number of vertices N is ruled by the topological constraints induced by the connectivity structure of the network. Even in the simple case of uncorrelated networks, we obtain an expression of the structural cut-off that is smaller than the natural cut-off obtained by means of extremal theory arguments. The obtained results are explicitly applied in the case of the configuration model to recover the size scaling of tadpoles and multiple edges.Received: 18 November 2003, Published online: 24 February 2004PACS: 89.75.-k Complex systems - 87.23.Ge Dynamics of social systems - 05.70.Ln Nonequilibrium and irreversible thermodynamics     

Efficiency and robustness in ant networks of galleries

Recent theoretical and empirical studies have focused on the topology of large networks of communication/interactions in biological, social and technological systems. Most of them have been studied in the scope of the small-world and scale-free networks theory. Here we analyze the characteristics of ant networks of galleries produced in a 2-D experimental setup. These networks are neither small-worlds nor scale-free networks and belong to a particular class of network, i.e. embedded planar graphs emerging from a distributed growth mechanism. We compare the networks of galleries with both minimal spanning trees and greedy triangulations. We show that the networks of galleries have a path system efficiency and robustness to disconnections closer to the one observed in triangulated networks though their cost is closer to the one of a tree. These networks may have been prevented to evolve toward the classes of small-world and scale-free networks because of the strong spatial constraints under which they grow, but they may share with many real networks a similar trend to result from a balance of constraints leading them to achieve both path system efficiency and robustness at low cost.Received: 16 July 2004, Published online: 26 November 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Hc Networks and genealogical trees - 87.23.Ge Dynamics of social systems     

Complex networks

We briefly describe the toolkit used for studying complex systems: nonlinear dynamics, statistical physics, and network theory. We place particular emphasis on network theory--the topic of this special issue--and its importance in augmenting the framework for the quantitative study of complex systems. In order to illustrate the main issues, we briefly review several areas where network theory has led to significant developments in our understanding of complex systems. Specifically, we discuss changes, arising from network theory, in our understanding of (i) the Internet and other communication networks, (ii) the structure of natural ecosystems, (iii) the spread of diseases and information, (iv) the structure of cellular signalling networks, and (v) infrastructure robustness. Finally, we discuss how complexity requires both new tools and an augmentation of the conceptual framework--including an expanded definition of what is meant by a quantitative prediction.Received: 12 November 2003, Published online: 14 May 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Da Systems obeying scaling laws     

Community analysis in social networks

We present an empirical study of different social networks obtained from digital repositories. Our analysis reveals the community structure and provides a useful visualising technique. We investigate the scaling properties of the community size distribution, and find that all the networks exhibit power law scaling in the community size distributions with exponent either -0.5 or -1. Finally we find that the networks community structure is topologically self-similar using the Horton-Strahler index.Received: 3 November 2003, Published online: 14 May 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Da Systems obeying scaling laws - 89.75.Hc Networks and genealogical trees     

The complexity and robustness of metro networks

Transportation systems, being real-life examples of networks, are particularly interesting to analyze from the viewpoint of the new and rapidly emerging field of network science. Two particular concepts seem to be particularly relevant: scale-free patterns and small-worlds. By looking at 33 metro systems in the world, this paper adapts network science methodologies to the transportation literature, and offers one application to the robustness of metros; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated. We find that most metros are indeed scale-free (with scaling factors ranging from 2.10 to 5.52) and small-worlds; they show atypical behaviors, however, with increasing size. In particular, the presence of transfer-hubs (stations hosting more than three lines) results in relatively large scaling factors. The analysis provides insights/recommendations for increasing the robustness of metro networks. Smaller networks should focus on creating transfer stations, thus generating cycles to offer alternative routes. For larger networks, few stations seem to detain a certain monopole on transferring, it is therefore important to create additional transfers, possibly at the periphery of city centers; the Tokyo system seems to remarkably incorporate these properties.     

Modeling the world-wide airport network

Recently, we have presented the first exhaustive analysis of the world-wide airport network. Two important results of that study are that: (i) the world-wide airport network is a small-world network with power-law decaying degree and betweenness centrality distributions; (ii) the most connected cities (largest degree) are typically not the most central cities (largest betweenness centrality). This second finding is particularly significant because of results demonstrating that nodes with high betweenness tend to play a more important role in keeping networks connected than those with high degree. Here, we investigate if current network models can explain this finding and we show that they cannot. Thus, we propose a new model that explains this behavior in terms of the geo-political constraints that affect the growth of the airport network. We further hypothesize that in other infrastructures, affected by similar geo-political constraints, critical locations might not coincide with highly-connected hubs.Received: 14 January 2004, Published online: 14 May 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Da Systems obeying scaling laws - 89.40.Dd Air transporation     

Multi-component static model for social networks

The static model was introduced to generate a scale-free network. In the model, N number of vertices are present from the beginning. Each vertex has its own weight, representing how much the vertex is influential in a system. The static model, however, is not relevant, when a complex network is composed of many modules such as communities in social networks. An individual may belong to more than one community and has distinct weights for each community. Thus, we generalize the static model by assigning a q-component weight on each vertex. We first choose a component among the q components at random and a pair of vertices is linked with a color according to their weights of the component as in the static model. A (1-f) fraction of the entire edges is connected following this way. The remaining fraction f is added with (q + 1)-th color as in the static model but using the maximum weights among the q components each individual has. The social activity with such maximum weights is an essential ingredient to enhance the assortativity coefficient as large as the ones of real social networks.Received: 27 October 2003, Published online: 17 February 2004PACS: 89.65.-s Social and economic systems - 89.75.Hc Networks and genealogical trees - 89.75.Da Systems obeying scaling laws     

Number of loops of size h in growing scale-free networks

The hierarchical structure of scale-free networks has been investigated focusing on the scaling of the number N(h)(t) of loops of size h as a function of the system size. In particular, we have found the analytic expression for the scaling of N(h)(t) in the Barabási-Albert (BA) scale-free network. We have performed numerical simulations on the scaling law for N(h)(t) in the BA network and in other growing scale-free networks, such as the bosonic network and the aging nodes network. We show that in the bosonic network and in the aging node network the phase transitions in the topology of the network are accompained by a change in the scaling of the number of loops with the system size.     

Coulomb effects in semiconductor quantum dots

This paper presents a new model for the Internet graph (AS graph) based on the concept of heuristic trade-off optimization, introduced by Fabrikant, Koutsoupias and Papadimitriou in [5] to grow a random tree with a heavily tailed degree distribution. We propose here a generalization of this approach to generate a general graph, as a candidate for modeling the Internet. We present the results of our simulations and an analysis of the standard parameters measured in our model, compared with measurements from the physical Internet graph.Received: 9 February 2004, Published online: 14 May 2004PACS: 89.75.-k Complex systems - 89.75.Hc Networks and genealogical trees - 89.75.Da Systems obeying scaling laws - 89.75.Fb Structures and organization in complex systems - 89.65.Gh Economics; econophysics, financial markets, business and managementLRI: http: //www.lri.fr/~ihameli; CNRS, LIP, ENS Lyon : http: //www.ens-lyon.fr/~nschaban     

Structure of cycles and local ordering in complex networks

We study the properties of quantities aimed at the characterization of grid-like ordering in complex networks. These quantities are based on the global and local behavior of cycles of order four, which are the minimal structures able to identify rectangular clustering. The analysis of data from real networks reveals the ubiquitous presence of a statistically high level of grid-like ordering that is non-trivially correlated with the local degree properties. These observations provide new insights on the hierarchical structure of complex networks.Received: 6 November 2003, Published online: 17 February 2004PACS: 89.75.-k Complex systems - 89.75.Fb Structures and organization in complex systems     

Universality in food webs

Among recently studied real-world networks, food webs are particularly interesting since they provide an example of biological organization at the largest scale, namely that of ecological communities. Quite surprisingly, recent results reveal that food webs do not display those properties which are observed in almost all other networks, such as a scale-free degree distribution and a large clustering coefficient. However, when food webs are regarded from the point of view of trasportation networks, it is possible to uncover very interesting scaling properties which are displayed by other trasportation systems, namely vascular and river networks. While other topological properties appear to vary across different webs depending on specific aspects, such scaling relations are universal. An interpretation of these results in terms of the interplay of universal and nonuniversal mechanisms in food web evolution is suggested.Received: 26 December 2003, Published online: 14 May 2004PACS: 87.23.-n Ecology and evolution - 89.75.-k Complex systems - 05.65. + b Self-organized systems     

Scaling of mean first-passage time as efficiency measure of nodes sending information on scale-free Koch networks

Random walks on complex networks, especially scale-free networks, have attracted considerable interest in the past few years. A lot of previous work showed that the average receiving time (ART), i.e., the average of mean first-passage time (MFPT) for random walks to a given hub node (node with maximum degree) averaged over all starting points in scale-free small-world networks exhibits a sublinear or linear dependence on network order N (number of nodes), which indicates that hub nodes are very efficient in receiving information if one looks upon the random walker as an information messenger. Thus far, the efficiency of a hub node sending information on scale-free small-world networks has not been addressed yet. In this paper, we study random walks on the class of Koch networks with scale-free behavior and small-world effect. We derive some basic properties for random walks on the Koch network family, based on which we calculate analytically the average sending time (AST) defined as the average of MFPTs from a hub node to all other nodes, excluding the hub itself. The obtained closed-form expression displays that in large networks the AST grows with network order as N ln N, which is larger than the linear scaling of ART to the hub from other nodes. On the other hand, we also address the case with the information sender distributed uniformly among the Koch networks, and derive analytically the global mean first-passage time, namely, the average of MFPTs between all couples of nodes, the leading scaling of which is identical to that of AST. From the obtained results, we present that although hub nodes are more efficient for receiving information than other nodes, they display a qualitatively similar speed for sending information as non-hub nodes. Moreover, we show that that AST from a starting point (sender) to all possible targets is not sensitively affected by the sender’s location. The present findings are helpful for better understanding random walks performed on scale-free small-world networks.     

Networks of equities in financial markets

We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the method can be used to falsify widespread market models by directly comparing the topological properties of networks of real and artificial markets.Received: 26 November 2003, Published online: 14 May 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Hc Networks and genealogical trees - 89.65.Gh Economics; econophysics, financial markets, business and management     

Dynamics of threshold network on non-trivial distribution degree

The dynamics of a threshold network (TN) with thermal noise on scale-free, random-graph, and small-world topologies are considered herein. The present analytical study clarifies that there is no phase transition independent of network structure if temperature T = 0, threshold h = 0 and the probability distribution degree P(k) satisfies P(0) = D = 0. The emergence of phase transition involving three parameters, T, h and D is also investigated. We find that a TN with moderate thermal noise extends the regime of ordered dynamics, compared to a TN in the T = 0 regime or a Random Boolean Network (RBN). A TN can be continuously reduced to an expression of RBN in the infinite T limit.Received: 25 February 2004, Published online: 12 August 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.20.Hh World Wide Web, Internet - 05.70.Fh Phase transitions: general studies     

Self-contained algorithms to detect communities in networks

The investigation of community structures in networks is an important issue in many domains and disciplines. In this paper we present a new class of local and fast algorithms which incorporate a quantitative definition of community. In this way the algorithms for the identification of the community structure become fully self-contained and one does not need additional non-topological information in order to evaluate the accuracy of the results. The new algorithms are tested on artificial and real-world graphs. In particular we show how the new algorithms apply to a network of scientific collaborations both in the unweighted and in the weighted version. Moreover we discuss the applicability of these algorithms to other non-social networks and we present preliminary results about the detection of community structures in networks of interacting proteins.Received: 7 November 2003, Published online: 14 May 2004PACS: 89.75.Hc Networks and genealogical trees - 87.23.Ge Dynamics of social systems - 87.90. + y Other topics in biological and medical physics     

Extremum statistics in scale-free network models

We investigate the statistics of the most connected node in scale-free networks. For a scale-free network model with homogeneous nodes, we show by means of extensive simulations that the exponential truncation, due to the finite size of the network, of the degree distribution governs the scaling of the extreme values and that the distribution of maxima follows the Gumbel statistics. For a scale-free network model with heterogeneous nodes, we show that scaling no longer holds and that the truncation of the degree distribution no longer controls the maxima distribution.     

Self-similar scaling of density in complex real-world networks

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations–network coarse-graining–and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks [P.J. Laurienti, K.E. Joyce, Q.K. Telesford, J.H. Burdette, S. Hayasaka, Universal fractal scaling of self-organized networks, Physica A 390 (20) (2011) 3608–3613] and imply an existence of a scale-free density also within–among different self-similar scales of–complex real-world networks. The latter further improves the comprehension of self-similar structure in large real-world networks with several possible applications.     

A neighbourhood evolving network model

Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabási–Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabási–Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development.     

Thermodynamic properties of an alternating-spin (1/2,1) two-leg ladder

Most complex networks serve as conduits for various dynamical processes, ranging from mass transfer by chemical reactions in the cell to packet transfer on the Internet. We collected data on the time dependent activity of five natural and technological networks, finding evidence of orders of magnitude differences in the fluxes of individual nodes. This dynamical inhomogeneity reflects the emergence of localized high flux regions or hot spots, carrying an overwhelming fraction of the networks activity. We find that each system is characterized by a unique scaling law, coupling the flux fluctuations with the total flux on individual nodes, a result of the competition between the systems internal collective dynamics and changes in the external environment. We propose a method to separate these two components, allowing us to predict the relevant scaling exponents. As high fluctuations can lead to dynamical bottlenecks and jamming, these findings have a strong impact on the predictability and failure prevention of complex transportation networks.Received: 25 October 2003, Published online: 17 February 2004PACS: 89.75.-k Complex systems - 89.75.Da Systems obeying scaling laws - 05.40.-a Fluctuation phenomena, random processes, noise, and Brownian motion