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The scale-free network shown in the small world phenomenon indicates that our human society consists of a small number of people who play the role of hubs linked with many nodes (persons) and a large number of people as nodes linked with few nodes. From our analysis of a large-scale online community - SourceForge.net - which has a large number of developers and projects, we have found that SourceForge also exists as a scale-free network. That is, only a minority of developers joins many projects and has rich links with other developers, while the majority joins few projects and has very limited social relations with others. The goal of our study is to build a system that supports knowledge collaboration in a large-scale online community of software development projects. In this paper, we discuss the challenges of supporting knowledge collaboration in such a large online community that is a scale-free network and then introduce the prototype system called D-SNS (dynamic social networking system)., APSEC'05 : 12th Asia-Pacific Software Engineering Conference, 15-17 Dec. 2005, Taipei, Taiwan

Evolutionary computation algorithms allow solving optimization problems through defined iterations and stages. One of the most commonly employed techniques for this type of problem is differential evolution, which contains properties of small-world complex networks, whose study is important because of the results they generate for optimization problems. Considering the results obtained in previous works, which propose an evolutionary algorithm guided by complex small-world networks, a proposal is defined which contains complex scale-free networks, with the purpose of validating the averages generated by complex networks against the results obtained by the traditional evolutionary algorithm. An experiment was defined which allows evaluating the performance of the proposed model and that of the evolutionary algorithm by means of statistic indicators. Four optimization problems (Ackley, Beale, Camel, and Sphere) were also used to evaluate the hypothesis in the proposed model, its convergence, and the reduction of execution times compared to the base model. It was observed that the scale-free complex networks generated better averages than the traditional evolutionary algorithm and the small-world networks because they use a connection preferential mechanism between their nodes and guide the combination of individuals (solutions), thus improving the convergence rate and the performance of the evolutionary algorithm in general., Los algoritmos de computación evolutiva permiten solucionar problemas de optimización a partir de iteraciones y etapas definidas. Una de las técnicas más utilizadas para este tipo de problemas es la evolución diferencial, que contiene propiedades de redes complejas de pequeño mundo, cuyo estudio es importante por los resultados que generan a los problemas de optimización. Teniendo en cuenta los resultados obtenidos en trabajos previos, en los que se propone un algoritmo evolutivo guiado por redes complejas de pequeño mundo, se define una propuesta que incluye redes complejas libres de escala, con el fin de validar los promedios generados por las redes complejas frente a los resultados presentados por el algoritmo evolutivo tradicional. Se definió un experimento que permite evaluar el desempeño del modelo propuesto y el del algoritmo evolutivo a través de indicadores estadísticos. También se utilizaron cuatro problemas de optimización (Ackley, Beale, Camel y Sphere) para evaluar la hipótesis en el modelo propuesto, su convergencia y la disminución de tiempos de ejecución frente al modelo base. Se observó que las redes complejas libres de escala generan mejores promedios que el algoritmo evolutivo tradicional y las redes complejas de pequeño mundo porque utilizan un mecanismo de conexión preferencial entre sus nodos y guían la combinación de individuos (soluciones), mejorando la tasa de convergencia y el rendimiento del algoritmo evolutivo en general.

Evolutionary computation algorithms allow solving optimization problems through defined iterations and stages. One of the most commonly employed techniques for this type of problem is differential evolution, which contains properties of small-world complex networks, whose study is important because of the results they generate for optimization problems. Considering the results obtained in previous works, which propose an evolutionary algorithm guided by complex small-world networks, a proposal is defined which contains complex scale-free networks, with the purpose of validating the averages generated by complex networks against the results obtained by the traditional evolutionary algorithm. An experiment was defined which allows evaluating the performance of the proposed model and that of the evolutionary algorithm by means of statistic indicators. Four optimization problems (Ackley, Beale, Camel, and Sphere) were also used to evaluate the hypothesis in the proposed model, its convergence, and the reduction of execution times compared to the base model. It was observed that the scale-free complex networks generated better averages than the traditional evolutionary algorithm and the small-world networks because they use a connection preferential mechanism between their nodes and guide the combination of individuals (solutions), thus improving the convergence rate and the performance of the evolutionary algorithm in general., Los algoritmos de computación evolutiva permiten solucionar problemas de optimización a partir de iteraciones y etapas definidas. Una de las técnicas más utilizadas para este tipo de problemas es la evolución diferencial, que contiene propiedades de redes complejas de pequeño mundo, cuyo estudio es importante por los resultados que generan a los problemas de optimización. Teniendo en cuenta los resultados obtenidos en trabajos previos, en los que se propone un algoritmo evolutivo guiado por redes complejas de pequeño mundo, se define una propuesta que incluye redes complejas libres de escala, con el fin de validar los promedios generados por las redes complejas frente a los resultados presentados por el algoritmo evolutivo tradicional. Se definió un experimento que permite evaluar el desempeño del modelo propuesto y el del algoritmo evolutivo a través de indicadores estadísticos. También se utilizaron cuatro problemas de optimización (Ackley, Beale, Camel y Sphere) para evaluar la hipótesis en el modelo propuesto, su convergencia y la disminución de tiempos de ejecución frente al modelo base. Se observó que las redes complejas libres de escala generan mejores promedios que el algoritmo evolutivo tradicional y las redes complejas de pequeño mundo porque utilizan un mecanismo de conexión preferencial entre sus nodos y guían la combinación de individuos (soluciones), mejorando la tasa de convergencia y el rendimiento del algoritmo evolutivo en general.

Neighborhood area networks (NANs) are critical infrastructure in smart grid to support communications. With the development of wireless communication technologies, there is a great potential for large-scale wireless-mesh NANs to be deployed to smart grid systems. While there exist theoretical models of complex networks, they are not directly applicable in the generation of network topology for wireless-mesh NANs due to their lack of some specifications unique in smart grid, leading to difficulties in theoretical studies of smart grid NANs. Among various topological structures, scale-free networks are considered as a potential resolution. This paper proposes a systematic approach to generate scale-free topology of wireless-mesh NANs in smart grid. A theoretical analysis confirms the scale-free feature of the network topology generated from the approach. To reveal the characteristics of our approach in comparison with widely used models of general complex networks, simulations are conducted on various network parameters including network diameter, average path length, clustering coefficient, and fractal dimension. The simulation results show that our approach for generating wireless-mesh NAN topology is fundamentally different from these compared models, and thus is a new type of complex-network model. Specifically, our model is suitable for wireless-mesh NANs in smart grid.

Sexually transmitted infections (STIs) are an ongoing public health concern. Despite many efforts, STIs have not been eradicated. Most STIs are infected through “horizontal” sexual contact and “vertical” mother-to-child transmission. We therefore need to explore the mathematical relationship between horizontal and vertical transmissions, which is necessary for the strategic eradication of STIs. Here, we constructed a simple model to demonstrate the infection dynamics of STIs with vertical transmission. We proposed a new formulation of the basic reproduction number (R0) for STIs over generations, and showed that vertical transmission exerts a smaller effect on the R0 than horizontal transmission. We also performed agent-based simulations to validate our theoretical predictions., Applied Mathematics and Computation, 348, pp.363-370; 2019

The rapid propagation of computer virus is one of the greatest threats to current cybersecurity. This work deals with the optimal control problem of virus propagation among computers and external devices. To formulate this problem, two control strategies are introduced: (a) external device blocking, which means prohibiting a fraction of connections between external devices and computers, and (b) computer reconstruction, which includes updating or reinstalling of some infected computers. Then the combination of both the impact of infection and the cost of controls is minimized. In contrast with previous works, this paper takes into account a state-based cost weight index in the objection function instead of a fixed one. By using Pontryagin's minimum principle and a modified forward-backward difference approximation algorithm, the optimal solution of the system is investigated and numerically solved. Then numerical results show the flexibility of proposed approach compared to the regular optimal control. More numerical results are also given to evaluate the performance of our approach with respect to various weight indexes.

Scale-free percolation is a percolation model on Zd which can be used to model real-world networks. We prove bounds for the graph distance in the regime where vertices have infinite degrees. We fully characterize transience versus recurrence for dimension 1 and 2 and give sufficient conditions for transience in dimension 3 and higher. Finally, we show the existence of a hierarchical structure for parameters where vertices have degrees with infinite variance and obtain bounds on the cluster density.

Scale-free percolation is a percolation model on Zd which can be used to model real-world networks. We prove bounds for the graph distance in the regime where vertices have infinite degrees. We fully characterize transience versus recurrence for dimension 1 and 2 and give sufficient conditions for transience in dimension 3 and higher. Finally, we show the existence of a hierarchical structure for parameters where vertices have degrees with infinite variance and obtain bounds on the cluster density.

Geometric inhomogeneous random graphs (GIRGs) are a model for scale-free networks with underlying geometry. We study bootstrap percolation on these graphs, which is a process modelling the spread of an infection of vertices starting within a (small) local region. We show that the process exhibits a phase transition in terms of the initial infection rate in this region. We determine the speed of the process in the supercritical case, up to lower order terms, and show that its evolution is fundamentally influenced by the underlying geometry. For vertices with given position and expected degree, we determine the infection time up to lower order terms. Finally, we show how this knowledge can be used to contain the infection locally by removing relatively few edges from the graph. This is the first time that the role of geometry on bootstrap percolation is analysed mathematically for geometric scale-free networks.

Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.

Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.

In this paper we apply a susceptible-infected-susceptible (SIS) epidemic model to analyse data dissemination in opportunistic networks with heterogeneous setting of transmission parameters, as established in author's previous paper? . We obtained the estimation of the final epidemic size assuming that amount of data transferred between network nodes possesses a Pareto distribution, implying scale-free properties. In this context, more heterogeneity in susceptibility means the less severe epidemic progression, and, on the contrary, more heterogeneity in infectivity leads to more severe epidemics — assuming that the other parameter (either heterogeneity or susceptibility) stays fixed. The results are general enough to be useful for estimating the epidemic progression with no significant acquired immunity — in the cases where Pareto distribution holds.

The first contribution of this dissertation is the use of complex network theory for the vulnerability analysis of power systems, taking into considerations various electrical parameters of the power grid into forming a graph corresponding to the power grid. An algorithm is presented to find critical elements from any power grid once the load flow analysis data is available on the system. A relation between the vulnerability and stability is found to exist, i.e., removal of important components found from proposed methodology shows transient behavior in the power angle oscillations. Various standard IEEE test systems have been used to demonstrate the utility of the proposed method. The proposed betweenness centrality based method is novel than previously existing ones employing complex network framework in better capturing several electrical characteristics of the electricity grid as well in finding stable and unstable regions within the grid.Secondly, this thesis develops a modified flow based betweenness centrality measure to identify critical elements of a power system. The proposed method overcomes the limitation of concentrating on shortest paths in calculating centrality indices; instead the method considers all possible paths through which the power can flow from source nodes to load nodes, giving a more realistic modeling choice of the power grid. Standard IEEE test systems have been used to exhibit the utilization of the method in finding critical components of the grid.Finally, various centrality measures like degree centrality, closeness centrality, and betweenness centrality used in complex network framework based analysis are applied for the power grid application. New definitions of these measures are proposed to capture the realistic power flow scenario of the grid. A new matrix, which contains the information of dependency of bus pairs in a power system, is also presented. An algorithm of finding the bus dependency matrix from the system is demonstra

To efficiently enhance the synchronizability of a scale-free network by adding some edges, we numerically study the effect of edge-adding on the spectrum of the network Laplacian matrix. Based on the relation between the largest eigenvalue of the Laplacian matrix and the largest degree of the scale-free network, we show that adding a new edge to the node of largest degree will generally weaken the synchronizability of a scale-free network. We consequently propose a method to effectively enhance the network synchronizability by attaching the new edge to a node whose nearest-neighbors have small degrees. The effect of the new method is analyzed and demonstrated with comparisons.

We study first passage percolation (FPP) on the configuration model (CM) having power-law degrees with exponent ? ? [1, 2) and exponential edge weights. We derive the distributional limit of the minimal weight of a path between typical vertices in the network and the number of edges on the minimal-weight path, both of which can be computed in terms of the Poisson-Dirichlet distribution. We explicitly describe these limits via construction of infinite limiting objects describing the FPP problem in the densely connected core of the network. We consider two separate cases, the original CM, in which each edge, regardless of its multiplicity, receives an independent exponential weight, and the erased CM, for which there is an independent exponential weight between any pair of direct neighbors. While the results are qualitatively similar, surprisingly, the limiting random variables are quite different. Our results imply that the flow carrying properties of the network are markedly different from either the mean-field setting or the locally tree-like setting, which occurs as ? > 2, and for which the hopcount between typical vertices scales as log n. In our setting the hopcount is tight and has an explicit limiting distribution, showing that information can be transferred remarkably quickly between different vertices in the network. This efficiency has a down side in that such networks are remarkably fragile to directed attacks. These results continue a general program by the authors to obtain a complete picture of how random disorder changes the inherent geometry of various random network models; see Aldous and Bhamidi (2010), Bhamidi (2008), and Bhamidi, van der Hofstad and Hooghiemstra (2009)., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

We study first passage percolation (FPP) on the configuration model (CM) having power-law degrees with exponent ? ? [1, 2) and exponential edge weights. We derive the distributional limit of the minimal weight of a path between typical vertices in the network and the number of edges on the minimal-weight path, both of which can be computed in terms of the Poisson-Dirichlet distribution. We explicitly describe these limits via construction of infinite limiting objects describing the FPP problem in the densely connected core of the network. We consider two separate cases, the original CM, in which each edge, regardless of its multiplicity, receives an independent exponential weight, and the erased CM, for which there is an independent exponential weight between any pair of direct neighbors. While the results are qualitatively similar, surprisingly, the limiting random variables are quite different. Our results imply that the flow carrying properties of the network are markedly different from either the mean-field setting or the locally tree-like setting, which occurs as ? > 2, and for which the hopcount between typical vertices scales as log n. In our setting the hopcount is tight and has an explicit limiting distribution, showing that information can be transferred remarkably quickly between different vertices in the network. This efficiency has a down side in that such networks are remarkably fragile to directed attacks. These results continue a general program by the authors to obtain a complete picture of how random disorder changes the inherent geometry of various random network models; see Aldous and Bhamidi (2010), Bhamidi (2008), and Bhamidi, van der Hofstad and Hooghiemstra (2009)., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

In this paper we investigate how the dynamics of a set of coupled Random Boolean Netowrks is affected by the changes in their topology. The Multi Random Boolean Networks (MRBN) is a model for the interaction among Random Boolean Networks (RBN). A single RBN may be regarded as an abstraction of gene regulatory networks, thus MRBNs might represent collections of communicating cells e.g. in tissues or in bacteria colonies. Past studies have shown how the dynamics of classical RBNs in the critical regime is affected by such an interaction. Here we compare the behaviour of RBNs with random topology to that of RBNs with scale-free topology for different dynamical regimes.

We study the dynamics of the contact process, one of the simplest nonequilibrium stochastic processes, taking place on a scale-free network. We consider the network topology as annealed, i.e., all links are rewired at each microscopic time step, so that no dynamical correlation can build up. This is a practical implementation of the absence of correlations assumed by mean-field approaches. We present a detailed analysis of the contact process in terms of a Langevin equation, including explicitly the effects of stochastic fluctuations in the number of particles in finite networks. This allows us to determine analytically the survival time for spreading experiments and the density of active sites in surviving runs. The fluctuations in the topological structure induce anomalous scaling effects with respect to the system size when the degree distribution has a hard upper bound. When the upper bound is soft, the presence of outliers with huge connectivity perturbs the picture even more, inducing an apparent shift of the critical point. In light of these findings, recent theoretical and numerical results in the literature are critically reviewed., Postprint (author's final draft)

Uno de los principales problemas en la investigación sobre generación automática de resúmenes (GAR) es la falta de utilización de conocimiento de dominio, que se refleja en la incorrecta interpretación semántica del documento y la baja calidad de los resúmenes obtenidos. En este trabajo se propone un método de extracción de oraciones para la GAR de artículos biomédicos, mediante el mapeo del documento a los conceptos de la ontología UMLS, y la representación del documento y de sus oraciones como grafos. La selección de las oraciones relevantes se realiza a partir de la conectividad de los conceptos que contienen en el grafo del documento. Se muestran los resultados empíricos preliminares de la aplicación de distintas heurísticas para la selección de las oraciones del resumen, y se identifican algunos problemas y líneas de trabajo futuras., One of the main problems in research on automatic summarization is the inaccurate semantic interpretation of the source, which is reflected in the deficiencies shown by the resulting summaries. Using specific domain knowledge, as that supplied by ontologies, can considerably alleviate the problem. In this paper, we introduce an ontology-based extractive method for summarization. It is based on mapping the text to concepts in the ontology and representing the document as a scale-free graph. To assess the importance of the sentences we compute the centrality of their concepts in the text. We have applied our approach to summarize scientific biomedical literature, taking advantage from free resources as UMLS. Preliminary empirical results are presented and pending problems are identified.

Uno de los principales problemas en la investigación sobre generación automática de resúmenes (GAR) es la falta de utilización de conocimiento de dominio, que se refleja en la incorrecta interpretación semántica del documento y la baja calidad de los resúmenes obtenidos. En este trabajo se propone un método de extracción de oraciones para la GAR de artículos biomédicos, mediante el mapeo del documento a los conceptos de la ontología UMLS, y la representación del documento y de sus oraciones como grafos. La selección de las oraciones relevantes se realiza a partir de la conectividad de los conceptos que contienen en el grafo del documento. Se muestran los resultados empíricos preliminares de la aplicación de distintas heurísticas para la selección de las oraciones del resumen, y se identifican algunos problemas y líneas de trabajo futuras., One of the main problems in research on automatic summarization is the inaccurate semantic interpretation of the source, which is reflected in the deficiencies shown by the resulting summaries. Using specific domain knowledge, as that supplied by ontologies, can considerably alleviate the problem. In this paper, we introduce an ontology-based extractive method for summarization. It is based on mapping the text to concepts in the ontology and representing the document as a scale-free graph. To assess the importance of the sentences we compute the centrality of their concepts in the text. We have applied our approach to summarize scientific biomedical literature, taking advantage from free resources as UMLS. Preliminary empirical results are presented and pending problems are identified.

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and prot

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and prot

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and prot

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and prot

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and prot

In Australia more than 300 vertebrates, including 43 insectivorous bat species, depend on hollows in habitat trees for shelter, with many species using a network of multiple trees as roosts. We used roost-switching data on white-striped freetail bats (Tadarida australis; Microchiroptera: Molossidae) to construct a network representation of day roosts in suburban Brisbane, Australia. Bats were caught from a communal roost tree with a roosting group of several hundred individuals and released with transmitters. Each roost used by the bats represented a node in the network, and the movements of bats between roosts formed the links between nodes. Despite differences in gender and reproductive stages, the bats exhibited the same behavior throughout three radiotelemetry periods and over 500 bat days of radio tracking: each roosted in separate roosts, switched roosts very infrequently, and associated with other bats only at the communal roost. This network resembled a scale-free network in which the distribution of the number of links from each roost followed a power law. Despite being spread over a large geographic area (>200 km2), each roost was connected to others by less than three links. One roost (the hub or communal roost) defined the architecture of the network because it had the most links. That the network showed scale-free properties has profound implications for the management of the habitat trees of this roosting group. Scale-free networks provide high tolerance against stochastic events such as random roost removals but are susceptible to the selective removal of hub nodes. Network analysis is a useful tool for understanding the structural organization of habitat tree usage and allows the informed judgment of the relative importance of individual trees and hence the derivation of appropriate management decisions. Conservation planners and managers should emphasize the differential importance of habitat trees and think of them as being analogous to vital servi

The scale-free network shown in the small world phenomenon indicates that our human society consists of a small number of people who play the role of hubs linked with many nodes (persons) and a large number of people as nodes linked with few nodes. From our analysis of a large-scale online community - SourceForge.net - which has a large number of developers and projects, we have found that SourceForge also exists as a scale-free network. That is, only a minority of developers joins many projects and has rich links with other developers, while the majority joins few projects and has very limited social relations with others. The goal of our study is to build a system that supports knowledge collaboration in a large-scale online community of software development projects. In this paper, we discuss the challenges of supporting knowledge collaboration in such a large online community that is a scale-free network and then introduce the prototype system called D-SNS (dynamic social networking system).

The scale-free network shown in the small world phenomenon indicates that our human society consists of a small number of people who play the role of hubs linked with many nodes (persons) and a large number of people as nodes linked with few nodes. From our analysis of a large-scale online community - SourceForge.net - which has a large number of developers and projects, we have found that SourceForge also exists as a scale-free network. That is, only a minority of developers joins many projects and has rich links with other developers, while the majority joins few projects and has very limited social relations with others. The goal of our study is to build a system that supports knowledge collaboration in a large-scale online community of software development projects. In this paper, we discuss the challenges of supporting knowledge collaboration in such a large online community that is a scale-free network and then introduce the prototype system called D-SNS (dynamic social networking system)., conference paper