@incollection{statphys23_1118, title = {Reliable regulatory dynamics from network evolution}, address = {Genova, Italy}, author = {S. Braunewell and S. Bornholdt}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1118}, abstract = {Living organisms have to function reliably in spite of the molecular fluctuations that are omnipresent in biochemical systems. This is a necessary requirement for survival and thus for selection in an evolutionary setting. On the other hand, it is still largely unknown how intricate networks of stochastic components can work together in a way that their outcome is dependable. In a simple Boolean model we investigate how the structure of a network as a whole can account for reliability. Reliability of a limit cycle attractor can be defined as the ability of a system to function in a quasi-deterministic fashion despite noisy transmission times. We introduce an evolutionary process that selects networks for reliability by a simple deterministic criterion. We find that our evolutionary process can quickly drive networks to reliability by significantly changing the structure of the attractor landscape. We also find that with high probability a network with a given `functional' attractor can be made reliable while retaining this attractor.}, biburl = {http://www.bibsonomy.org/bibtex/285cdae8e47d63a26f5a05dc0f7b8b599/statphys23}, keywords = {networks structure gene complex dynamics regulation topic-10 statphys23 network evolution} } @incollection{statphys23_1043, title = {Hierarchy and feedback in transcription networks}, address = {Genova, Italy}, author = {M. Cosentino Lagomarsino and B. Bassetti and F. Bassetti and H. Isambert}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1043}, abstract = {We present statistical null-models for the structure and the growth of transcription networks, through a data-analysis approach that aims to evaluate feedback and hierarchy in an evolutionary perspective. The analysis is used to establish concrete biological trends, such as whether transcriptional autoregulators have mostly arisen through duplication, or if the existing hierarchical layers of computation are evolutionarily conserved.}, biburl = {http://www.bibsonomy.org/bibtex/272a91eeaac08d5b8da87c6be6352800b/statphys23}, keywords = {transcriptional models regulation topic-10 statphys23 bioinformatics network evolution} } @incollection{statphys23_1020, title = {A Complex Network Model of Words to Describe the Dynamics of Text Construction}, address = {Genova, Italy}, author = {S.M.G. Caldeira and D.M.B. Coutinho and G.M. Teixeira}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1020}, abstract = {This work explores interdisciplinary dialogue between Physics and Psychoanalysis on the dynamics of text construction. Human language must be considered a complex object of knowledge. Physics offers concepts and instruments that allow modeling the language apparatus as a complex network. Language complexity is evidenced by an intricate system composed of elements (words) that interact in small groups (sentences), arriving at a bigger, auto-organized organism (text), thus producing emergent order (sense). Each sentence is a conceptual unit, where new sentences are connected with old ones by means of shared words, forming a network. Analyzing unconscious phenomena, Freud describes a “reticular fabric”, a network with vertices, edges and interstices, emphasizing quantitative differences between tracks through which the neuron information passes, generating preferential ways, where “difference of essence is substituted by one of destination and place”. Freud’s hypothesis is that, in speech and writing, the process of choosing words is unconscious and determined by easiness of connection between representans (words corresponding to objects, not as meaning, but as marks). With networks theory, we analyzed different samples of written texts, in search of emergent properties. To allow building networks, the texts received a previous treatment to eliminate grammatical words and reduce them to canonic form. Statistical analyses used Degree Distribution, Diameter, Frequency of Pairs, Critical Centrality and Betweenness, as measures for identifying words of bigger value for the network, as well as ratio of new words. The dynamics of written text construction was analyzed by adding new sentences and words, measuring parameters in each stage. All texts presented a redundancy pattern responsible for the topology of the network, but the expanded texts continued to present new concepts, suggesting a similar behavior between them, including oral discourses. The exception was Joyce’s Ulisses whose new word increases as a function of new sentences presented an exponent extremely high. In line with Freud’s hypothesis, results indicate that the network topology is composed by frequency of word repetition and not by structure of sentences or use of grammatical words.}, biburl = {http://www.bibsonomy.org/bibtex/29f981e8e4703173c0b24b57ce76371cc/statphys23}, keywords = {textual complex apparatus topic-11 statphys23 language analysis network} } @incollection{statphys23_1019, title = {Structure and evolution of online social relationship}, address = {Genova, Italy}, author = {B. Kahng and K. Goh and Y. Eom and H. Jeong and D. Kim}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1019}, abstract = {With the advancement in the information age, people are using electronic media more frequently for communications, and social relationships are also increasingly resorting to online channels. While extensive studies on traditional social networks have been carried out, little has been done on online social networks. Here we analyze the structure and evolution of online social relationship by examining the temporal records of a bulletin board system in a university. The obtained network exhibits interesting features: In contrast to a typical community network, the BBS network contains hub members who participate in many boards simultaneously but are strongly tied, that is, they have a large degree and betweenness centrality and provide communication channels between communities. On the other hand, intracommunities are rather homogeneously and weakly connected. Such a structure, which has never been emiprically characterized in the past, might provide a new perspective on the social opinion formation in this digital era. We also study the temporal evolution of such a network and compare it with existing models for weighted complex networks.}, biburl = {http://www.bibsonomy.org/bibtex/259f9c82972ed0014d34d0d45bbf54e95/statphys23}, keywords = {relationship information complex social topic-11 era statphys23 network online} } @incollection{statphys23_0977, title = {Analysis of Topological Characteristics of Huge Online Social Networking Services}, address = {Genova, Italy}, author = {Y.Y. Ahn and S. Han and H. Kwak and S. Moon and H. Jeong}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=977}, abstract = {Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's \textit{ilchon} (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Each of the two exponents seems to correspond to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.}, biburl = {http://www.bibsonomy.org/bibtex/21fc65336ca05ecdd0e39bb86eea06b7d/statphys23}, keywords = {complex social topic-11 statphys23 network} } @incollection{statphys23_0970, title = {Group dynamics in the coevolution of states and networks.}, address = {Genova, Italy}, author = {F. Vazquez and J. Gonzalez Avella and V. Eguiluz and M. San Miguel}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=970}, abstract = {We study the coevolution of network structure and node states in an opinion dynamics model for the formation of communities in a population of individuals. In a single interaction a pair of connected nodes is chosen at random. If their states are similar enough they interact, becoming even more similar; but if they are totally different, one of the individuals detaches its link from its neighbor and attaches it to a randomly chosen node. The dynamics drives the system towards either an absorbing configuration composed by disconnected clusters whose agents share the same opinion or a stationary active state with a fraction of links continuously being rewired. Within the absorbing phase, we found an anomalous order-disorder phase transition that seems to disappear in the thermodynamic limit. We observe that the nature of this transition is closely related to the crossover between the time scales that control the structure and the state dynamics of the system.}, biburl = {http://www.bibsonomy.org/bibtex/29a941f05259b21cab134756fb6df1d2a/statphys23}, keywords = {coevolution dynamics social topic-11 statphys23 network} } @incollection{statphys23_0963, title = {Modelling the behavior of individual websurfers}, address = {Genova, Italy}, author = {B.M.T. Gon\c Calves and J.J. Ramasco}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=963}, abstract = {We perform a large scale analysis of the behavior of individual users as they navigate through a World Wide Web domain. The logs of the webserver responsible for serving Emory University's web content under the \emph{emory.edu} domain and all subdomains over a period of $294$ consecutive days is analyzed. The resulting bipartite graph contained $3,179,671$ IP addresses connected to $2,562,398$ unique URLs through $53,582,121$ individual and timestamped page requests. Limited information on the referer URLs is also available. The topology and the dynamics of the network evolution is analyzed and both numerical and analytical modeling strategies are proposed. The resulting properties of the simulated network are compared with the original empirical results.}, biburl = {http://www.bibsonomy.org/bibtex/2ab06de286d5aae0d9062f7374c9315ad/statphys23}, keywords = {scale modeling www topic-11 statphys23 analysis network large} } @incollection{statphys23_0957, title = {How to combine amino acid sequences and neural networks to improve protein secondary structure prediction}, address = {Genova, Italy}, author = {G.A. Maino and F. Fini}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=957}, abstract = {One of the major goals in protein science is the prediction of the secondary structure of proteins starting from their amino acid sequence. This is a former step in solving the folding problem. We describe results based on the use of two neural networks, the former being a perceptron with one hidden layer, performing a supervised learning phase. Sequences of outputs from the first network are then introduced as inputs to the second network, which has a filtering effect. The networks are implemented in ANSI C language and are optimized to run on a cluster of workstations under the UNIX operating system with PVM protocol. Parallel implementation of the algorithms has been tested on the prediction of secondary and tertiary structure of proteins starting from their aminoacidic sequences, resulting in an overall accuracy better than 74% on a data base of 300 non redundant proteins. Moreover, a few important proteins, such as ribonuclease and mioglobin, are discussed in detail.}, biburl = {http://www.bibsonomy.org/bibtex/29a4335819a5441c3654a55a93f3ad306/statphys23}, keywords = {parallel structure protein prediction neural topic-10 folding computation statphys23 network} } @incollection{statphys23_0939, title = {Democratic particle motion for meta-basin transitions in supercooled water}, address = {Genova, Italy}, author = {E. La Nave and F. Sciortino and J.A. Rodriguez Fris and G. Appignanesi}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=939}, abstract = {We use molecular dynamics computer simulations to investigate the local motion of particles in a supercooled complex liquid and how this relates with the Potental Energy Landscape (PEL) exploration. A new powerful method to investigate the mechanisms controlling the PEL exploration is introduced. This new method, the Distance Matrix methods, has recently been successfully applied to the study of a simple liquids [Appignanesi et al.,Phys. Rev. Lett. 96, 057801 (2006)], allowing a clear definition of the metabasin concept and supporting the scenario of a random walk on metabasins for the long time diffusion. In this talk we apply this method in a study of supercooled water. We give a further confirmation that the motion of the system consists in the metabasin exploration, followed by a sharp crossing to a different metabasin. Furthermore, we focused on the crossing between metabasins. The new method allows us to easily identify the particles participating to the crossing. This particles result to form relatively compact clusters that act as cooperative relaxing units. This units could be candidates for the cooperatively rearranging regions proposed by Adam and Gibbs. Moreover, we focus on the hydrogen bond role in water diffusion, and identify the connection between particles participating to the relaxing units and their role in the tetrahedral hydrogen bond network.}, biburl = {http://www.bibsonomy.org/bibtex/28e5844c241211dc51eeefe09ae772f4c/statphys23}, keywords = {bond liquids hydrogen potential topic-6 statphys23 landscape network energy supercooled} } @incollection{statphys23_0935, title = {Topological phase transitions in networks give truncated power-law tails in degree distributions}, address = {Genova, Italy}, author = {H. Agrawal}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=935}, abstract = {In any given suitably ordered collection of networks $\{G\}$ major topological changes can be detected systematically by analyzing the variation of homogeneity $\Lambda$ with the parameter affecting the ordering, say $\xi$, where \[ \Lambda(G) = \frac{1+\bar{z}+\frac{1}{2}[1-P(z_{\max})]}{1+z_{\max}}, \] where the network $G$ has $P(z_{\max})$ fraction of hubs of degree $z_{\max}$ and average degree in the network is $\bar{z}$. These changes, detectable as phase transitions, are signaled by singular behavior of the second and higher derivatives of homogeneity with respect to the ordering parameter or another parameter that is smoothly isomorphic to it, i.e., \[ \frac{d^{n}\Lambda}{d\xi^{n}} = \pm\infty \] where $n \ge 2$. The case $n = 2$ corresponds to first-order and $n > 2$ corresponds to continuous phase transitions. We show that irrespective of whether the phase transitions are first-order or continuous, the networks corresponding to the point of transitions, or the flat regions surrounded by such points, have truncated power-law tails with the cumulative distributions behaving like $a x^{-\alpha} \exp(-b x^{\beta})$, where $x = (z+1)/(z_{\max}+1)$. Furthermore, for all finite sized networks the truncation function is an stretched exponential, i.e., $\beta \ne 1$. We also determine the order of the phase transitions from the observed behavior of the tails.}, biburl = {http://www.bibsonomy.org/bibtex/23982ee523e43fa2f2d378309f2f5f823/statphys23}, keywords = {systems networks complex order transitions homogeneity topic-11 statphys23 phase network} } @incollection{statphys23_0795, title = {Growing Network On Euclidean Space}, address = {Genova, Italy}, author = {M.O. Hase and J.F.F. Mendes}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=795}, abstract = {Graph properties, like degree distributions, clustering, shortest path lengths, et cetera, have been intensively examined in structure and dynamics of complex networks. Many real networks, however, have an additional property of being embedded in a metric arrangement, where the geographic distance between vertices affects graph properties. The present work proposes a model for growing network that takes into account the Euclidean distance between vertices: the probability that two vertices connect each other decreases with their distance. Starting from a central node at time $t=0$, the network is made to grow toward $m$ directions -- or branches. At each time step, taken to be $1$, $m$ new vertices are added to the network, one at each branch. The positions of the vertices are such that they are located at distance $1$ from the previous vertex in the same branch, and also at distance $1$ from the vertices that were born at the same time in adjacent branches -- see Figure 1. Each vertex, born at time $t+1$, links to just one of the $mt+1$ previous vertices with probability $r^{-\alpha}/N$, where $r$ is the Euclidean distance between the vertices, and $N$ is the normalization, which depends on the size of the network ($t$), $\alpha$, and $m$. The parameter $\alpha$ tunes the strength of the range of interaction, and different behaviour of some graph properties are displayed as $\alpha$ varies on the non - negative real axis. The function $\overline{k}(s,t)$, which is the mean degree of a vertex $s(1$, respectively. For $1\sim m\ll s\ll t$, the result for the $m=1$ case is recovered, while for the other limit, $1\ll s\ll t\ll m$, the mean degree increases as $\ln(t/s)$ (apart from a multiplicative term that depends on $\alpha$) for $0\leq\alpha\leq 2$. In the case $\alpha>2$, a special attention should be devoted to the parameter $mt^{1-\alpha}$. If $mt^{1-\alpha}\gg 1$, one still has a logarithmic behaviour of the mean degree, while for $mt^{1-\alpha}\ll 1$, $\overline{k}(s,t)$ is limited and tends to $2$, as expected, as $\alpha$ increases. These later results stress the fact that the number of branches does affect the graph property of the network.}, biburl = {http://www.bibsonomy.org/bibtex/2e5a312012f005049b50e69f9bfe0445d/statphys23}, keywords = {results exact topic-11 statphys23 growing space euclidean network} } @incollection{statphys23_0787, title = {Modelling colloidal suspensions: Gelation, network formation and phase separation.}, address = {Genova, Italy}, author = {E. Del Gado}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=787}, abstract = {In colloidal gels, the deep connection existing between their unusual dynamics and the open network characterizing their structure is still not understood. In the dramatic slowing down of the dynamics accompanying the gel formation, the experimental findings suggest that different relaxation mechanisms interplay at a microscopic level, which have not been elucidated yet. Moreover, in colloidal suspensions at low volume fractions the underlying thermodynamics may significantly interplay and/or compete with gel formation via phase separation processes. I will review some recent developments based on molecular dynamics simulations of model systems. In particular I will illustrate the case of a colloidal suspension with competing attraction and repulsion, where gelation results to be directly coupled to microphase separation. Then I will discuss a model in which directional interactions are able to produce a persistent gel network at relatively high temperatures, where phase separation does not occur, without imposing a local functionality of the meso-particle. The numerical study shows in this case that the formation of the gel network does induce a non-trivial length scale dependence of the dynamics in a simple model for colloidal gels: In the incipient gel, the relaxation at high wave vectors is due to the fast cooperative motion of pieces of the gel structure, whereas at low wave vectors the overall rearrangements of the heterogeneous gel make the system relax via a stretched exponential decay of the time correlators. The coexistence of such diverse relaxation mechanisms is determined by the formation of the gel network (i.e. the onset of the elastic response of the system) and it is characterized by a typical crossover length which is of the order of the network mesh size.}, biburl = {http://www.bibsonomy.org/bibtex/2bcf28fdbc8a4d46ce8f7cbe73c69a8ac/statphys23}, keywords = {dynamics colloidal topic-7 statphys23 slow network gels formation} } @incollection{statphys23_0741, title = {Evolution Mechanisms of Weighted Complex networks : Growth and Reinforcement}, address = {Genova, Italy}, author = {Y. Eom and C. Jeon and B. Kahng and H. Jeong}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=741}, abstract = {Network evolution is a key issue of network study to understand design principle underlying network organization . We investigate the evolution mechanisms of weighted networks by analyzing time-dependent data of real weighted networks. We find power-law growth rates of networks and identify generalized kernel functions which drive network evolution. Based on observed results, we propose an evolving weighted network model. Our model successfully reproduce strength, degree and weight distributions of real networks. In addition, we study weight-topology correlations in model and real weighted networks.}, biburl = {http://www.bibsonomy.org/bibtex/2240224d572eb5653f15e378a70ca30c2/statphys23}, keywords = {networks weighted topic-11 statphys23 network evolution} } @incollection{statphys23_0719, title = {Model for complex networks with degree-degree correlation}, address = {Genova, Italy}, author = {S.W. Kim and J.D. Noh}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=719}, abstract = {We introduce a model for complex networks with positive degree-degree correlation. In this model, one starts with $N$ nodes and then adds links successively between pairs of nodes selected in a specific way. If one selects nodes randomly among all nodes, one obtains a random network with no degree correlation. The positive correlation is generated by selecting nodes of the same degree preferentially. Structral properties of the model will be presented. We also report our numerical results on the percolation transition of the model network with the positive degree-degree correlation.}, biburl = {http://www.bibsonomy.org/bibtex/26dd52976d8f6e7770508ce447c4cf2f0/statphys23}, keywords = {complex transition topic-11 percolation correlation statphys23 network} } @incollection{statphys23_0718, title = {Social network analysis based on WWW search engine}, address = {Genova, Italy}, author = {S.H. Lee and P.J. Kim and Y.Y. Ahn and H. Jeong}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=718}, abstract = {Recently, massive digital records have made it possible to analyze a huge amount of data in social sciences, one of which is social network theory. We investigate social networks between people by extracting information on the World Wide Web. Using famous search engines such as Google, we construct weighted social networks where the nodes are the names of people and the weight of each link is assigned as the number of web pages including both of the names attached to the link. The weight distribution is found to be quite broad with the heavy-tail. The strength of a node, defined as the sum of weights over the node, is strongly correlated with the number of web pages including the single node. Furthermore, we suggest the quantity, called the effective degree, characterizing the homogeneity (or heterogeneity) of weight distribution for each node in the weighted network. Another way to quantify the importance of each node, based on the effective degree, is also introduced.}, biburl = {http://www.bibsonomy.org/bibtex/2a4d441aefbc16af427dfa1a943711f4e/statphys23}, keywords = {complex weighted search engine social topic-11 statphys23 network} } @incollection{statphys23_0714, title = {The Price of Anarchy in Transportation Networks}, address = {Genova, Italy}, author = {H.J. Youn and M.T. Gastner and H. Jeong}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=714}, abstract = {The price for traveling in a network paid by a single traveler often depends on decisions made by other passengers. In particular, the time spent on each link in the journey (e.g. a road or an airport) is known to increase steeply with the total number of users in real transportation networks. Different policies to assign passengers to routes through the network can affect the distribution of traffic and, hence, the delays experienced by individuals. It is known from game theory that users can cause unnecessarily long travel times to others and sometimes even themselves if allowed to individually seek the quickest paths. However, it remains unknown how inefficient such decisions based on everybody's self-interest are in real networks. Here we show that travel times in Boston's road network can be expected to be up to 30% longer than the ``Social Optimum''. To gain a better understanding under what circumstances users have to pay such a substantial ``price of anarchy'', we analyze different models for traffic networks suggested in the recent literature. A famous result in traffic optimization (known as Braess' paradox) states that removing links can sometimes reduce effective travel times. We find that this phenomenon exists in the Boston network, but improvements are too small to make link deletions a promising way for controling selfish behavior.}, biburl = {http://www.bibsonomy.org/bibtex/210e138742b5027ef032227fe02cc994d/statphys23}, keywords = {anarchy traffic price paradox optimum social topic-11 braess statphys23 nash equilibrium network} } @incollection{statphys23_0688, title = {Statistical Physics of Correlated Failures and Defaults}, address = {Genova, Italy}, author = {S. Mori and M. Hisakado}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=688}, abstract = {Systemic failure problems are hot topics in econophysics, financial engineering and computer engineering. For a long time, this area lacks good data and it was difficult to examine their modelings. Recently, empirical data analysis of correlated failures in Wide-area network systems and the researches on market quotes of credit derivatives have clarified their statistical properties. The probability function for the number of failures in storage systems has a long tail, which means that the failures are highly correlated. The implied loss functions of credit portfolios, which are estimated based on the market quotes of iTraxx-CJ, CDX-IG and iTraxx-Europe, also have the same nature. Credit markets expect that the defaults of the assets in the portfolio do not occur independently and their correlations are relatively strong. In this paper, we would like to review some results on a method to study the correlated failure data. In addition, we compare some theoretical models and understand their behaviors. At first, we show the general method to construct correlated binomial models. We introduce conditional correlations $\rho_{ij}$ and expectations $p_{ij}$, where the suffix ${}_{ij}$ means the condition that $i (j)$ of $N$ variables take $1 (\mbox{resp.} 0)$. Based on them, we show how to construct joint probability function and derive recursive relations, which are necessary conditions to ensure the probability conservation of the model. Next, by using the recursive relations we show how to calibrate the correlation structures ($\rho_{ij}$ and $p_{ij}$) based on the probability function. By the method, we compare the correlation structures of several probabilistic models. In addition, we also compare those of empirical probability functions and discuss the physical mechanism to induce them. References:\\ 1) M.Hisakado, K.Kistukawa and S.Mori, J.Phys.A:Math.Gen.39(2006) 15365-15378. \\ 2) S. Mori, K. Kitsukawa and M. Hisakado, Default Distribution and Credit Market Implications (arXiv:physics0609093).\\ 3) S.Mori, K.Kitsukawa, M.Hisakado, Moody's Correlated Binomial Default Distributions for Inhomogeneous Portfolios (arXiv:physics0603036).}, biburl = {http://www.bibsonomy.org/bibtex/24c7168513ca8cfd125a3fe8f3ddb8cb3/statphys23}, keywords = {risk systemic model binomial infection topic-11 correlation statphys23 network} } @incollection{statphys23_0629, title = {Mean Field Theory for Stochastic Dynamical Systems}, address = {Genova, Italy}, author = {A. Ichiki and M. Shiino}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=629}, abstract = {We investigate the statistical properties of stochastic systems in which the mean field approximation becomes exact at thermodynamic limit. Especially, we focus on the systems of coupled elements with temporally fluctuating coupling strength. Such systems, in general, has no energy function and thus defy the use of standard statistical techniques such as Boltzmann-Gibbs statistics.However the statistical properties in some classes of such systems can be investigated exactly analytically since the mean field approach becomes exact in such systems. We present two types of such examples: (i) coupled oscillator system with fluctuating coupling strength, (ii) associative memory neural network model with temporally fluctuating coupling strength. The first example are found to show the chaos-nonchaos nonequilibrium phase transition. This fact gives us a dynamical aspect of mean field theory for the system with fluctuating couplings. The second example are found to be analyzed by standard statistical methods, e. g., replica method or cavity method to investigate the equilibrium properties of the system. However the effect of the fluctuating couplings modifies the temperature of the system. This results are confirmed by the self-averaging property at thermodynamic limit. This fact gives us a static aspect of mean field theory for the system with fluctuating couplings.}, biburl = {http://www.bibsonomy.org/bibtex/2a74a4fa20f0e910cee20447707f9032b/statphys23}, keywords = {nonequilibrium neural topic-11 transition oscillators stochastic network field system coupled mean statphys23 phase} } @incollection{statphys23_0608, title = {Diffusive capture process on scale-free networks}, address = {Genova, Italy}, author = {S. Lee and S.H. Yook and Y. Kim and S. Yoon and H. Kim and D. Lee and Y. Ko}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=608}, abstract = {We study the dynamical properties of a diffusing lamb captured by a diffusing lion on the scale-free networks with various sizes of $N$. We find that the life time $\left$ of a lamb scales as $\left\sim N$ and the survival probability $S(N\rightarrow \infty,t)$ becomes finite on scale-free networks with degree exponent $\gamma>3$. However, $S(N,t)$ for $\gamma<3$ has a long-living tail on tree-structured scale-free networks and decays exponentially on looped scale-free networks. It suggests that the second moment of degree distribution $\left$ is the relevant factor for the dynamical properties in diffusive capture process. We numerically find that the normalized number of capture events at a node with degree $k$, $n(k)$, decreases as $n(k)\sim k^{-\sigma}$. When $\gamma<3$, $n(k)$ still increases anomalously for $k\approx k_{max}$, where $k_{max}$ is the maximum value of $k$ of given networks with size $N$. We analytically show that $n(k)$ satisfies the relation $n(k)\sim k^2P(k)$ for any degree distribution $P(k)$ and the total number of capture events $N_{tot}$ is proportional to $\left$, which causes the $\gamma$ dependent behavior of $S(N,t)$ and $\left$}, biburl = {http://www.bibsonomy.org/bibtex/249216bf652ed72e451cd308462b6b0a6/statphys23}, keywords = {process capture topic-11 scale-free diffusive statphys23 network} } @incollection{statphys23_0606, title = {Statistical properties of sub-network with weighted sampling method}, address = {Genova, Italy}, author = {Y. Ko and D. Lee and S. Yoon and S.M. Lee and S. Yook and Y. Kim}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi}, month = {9-13 July}, year = 2007, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=606}, abstract = {We study the statistical properties of the sampled networks by a random walker. We compare topological properties of the sampled networks such as degree distribution, degree-degree correlation, and clustering coefficient with those of the original networks. From the numerical results, we find that most of topological properties of the sampled networks are almost the same as those of the original networks for $\gamma\leq 3$. In contrast, we find that the degree distribution exponent of the sampled networks for $\gamma>3$ somewhat deviates from that of the original networks when the ratio of the sampled network size to the original network size becomes smaller. We also apply the sampling method to various real networks such as collaboration of movie actor, world wide web, and peer-to-peer networks. All topological properties of the sampled networks show the essentially same as the original real networks.}, biburl = {http://www.bibsonomy.org/bibtex/2cc69f3675663e057c96ebbbfb8b4424c/statphys23}, keywords = {weighted walks sampling random topic-11 scale-free statphys23 network} }