TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 6: Network Evolution and Process

In this class, we will discuss the nature of network evolution and some selected network processes. We will discuss graph generation algorithms that generate networks with different interesting characteristics. Optional : The Structure and Function of Complex Networks (chapter 8), M.E.J. Newman, SIAM Review 45 167--256 (2003); Optional: Emergence of Scaling in Random Networks, A.L. Barabasi and R. Albert, Science 286, 509 (1999)

WAIS Seminar:Modelling the Web: Examples of modelling language, network evolution and physical-social systems

Speaker(s): Prof. Steffen Staab Organiser: Dr Tim Chown Time: 23/05/2014 10:30-11:30 Location: B53/4025 Abstract The Web is constructed based on our experiences in a multitude of modalities: text, networks, images, physical locations are some examples. Understanding the Web requires from us that we can model these modalities as they appear on the Web. In this talk I will show some examples of how we model text, hyperlink networks and physical-social systems in order to improve our understanding and our use of the Web.

A physarum network evolution model based on IBTM

The traditional Cellular Automation-based Physarum model reveals the process of amoebic self-organized movement and self-adaptive network formation based on bubble transportation. However, a bubble in the traditional Physarum model often transports within active zones and has little change to explore newareas.And the efficiency of evolution is very low because there is only one bubble in the system. This paper proposes an improved model, named as Improved Bubble Transportation Model (IBTM). Our model adds a time label for each grid of environment in order to drive bubbles to explore newareas, and deploysmultiple bubbles in order to improve the evolving efficiency of Physarum network.We first evaluate the morphological characteristics of IBTM with the real Physarum, and then compare the evolving time between the traditional model and IBTM. The results show that IBTM can obtain higher efficiency and stability in the process of forming an adaptive network.

Network evolution and the spatiotemporal dynamics of knowledge sourcing

Knowledge accessing from external organisations is important to firms, especially entrepreneurial ones which often cannot generate internally all the knowledge necessary for innovation. There is, however, a lack of evidence concerning the association between the evolution of firms and the evolution of their networks. The aim of this paper is to begin to fill this gap by undertaking an exploratory analysis of the relationship between the vintage of firms and their knowledge sourcing networks. Drawing on an analysis of firms in the UK, the paper finds some evidence of a U-shaped relationship existing between firm age and the frequency of accessing knowledge from certain sources. Emerging entrepreneurial firms tend to be highly active with regard to accessing knowledge for a range of sources and geographic locations, with the rate of networking dropping somewhat during the period of peak firm growth. For instance, it is found that firms tend to less frequently access knowledge sources such as universities and research institutes in their own region during a stage of peak turnover growth. Overall, the results suggest a complex relationship between the lifecycle of the firm and its networking patterns. It is concluded that policymakers need to become more aware that network formation and utilisation by firms is likely to vary dependent upon their lifecycle position.

Network reconfiguration of distribution systems using metaheuristics and reliability measures

In an evermore competitive environment, power distribution companies need to continuously monitor and improve the reliability indices of their systems. The network reconfiguration (NR) of a distribution system is a technique that well adapts to this new deregulated environment for it allows improvement of system reliability indices without the onus involved in procuring new equipment. This paper presents a reliability-based NR methodology that uses metaheuristic techniques to search for the optimal network configuration. Three metaheuristics, i.e. Tabu Search, Evolution Strategy, and Differential Evolution, are tested using a Brazilian distribution network and the results are discussed. © 2009 IEEE.

Symmetry insights for design of supercomputer network topologies: roots and weights lattices

We present a family of networks whose local interconnection topologies are generated by the root vectors of a semi-simple complex Lie algebra. Cartan classification theorem of those algebras ensures those families of interconnection topologies to be exhaustive. The global arrangement of the network is defined in terms of integer or half-integer weight lattices. The mesh or torus topologies that network millions of processing cores, such as those in the IBM BlueGene series, are the simplest member of that category. The symmetries of the root systems of an algebra, manifested by their Weyl group, lends great convenience for the design and analysis of hardware architecture, algorithms and programs.

Collaborative Change: Environmental Jolt, Network (Re)Design and Firm Performance

Motivated by the need to understand which are the underlying forces that trigger network evolution, we develop a multilevel theoretical and empirically testable model to examine the relationship between changes in the external environment and network change. We refer to network change as the dissolution or replacement of an interorganizational tie, adding also the case of the formation of new ties with new or preexisting partners. Previous research has paid scant attention to the organizational consequences of quantum change enveloping entire industries in favor of an emphasis on continuous change. To highlight radical change we introduce the concept of environmental jolt. The September 11 terrorist attacks provide us with a natural experiment to test our hypotheses on the antecedents and the consequences of network change. Since network change can be explained at multiple levels, we incorporate firm-level variables as moderators. The empirical setting is the global airline industry, which can be regarded as a constantly changing network of alliances. The study reveals that firms react to environmental jolts by forming homophilous ties and transitive triads as opposed to the non jolt periods. Moreover, we find that, all else being equal, firms that adopt a brokerage posture will have positive returns. However, we find that in the face of an environmental jolt brokerage relates negatively to firm performance. Furthermore, we find that the negative relationship between brokerage and performance during an environmental jolt is more significant for larger firms. Our findings suggest that jolts are an important predictor of network change, that they significantly affect operational returns and should be thus incorporated in studies of network dynamics.

Overcoming network overload and redundancy in inter-organizational networks:the roles of potential and latent ties

This paper builds on Granovetter's distinction between strong and weak ties [Granovetter, M. S. 1973. The strength of weak ties. Amer. J. Sociol. 78(6) 1360–1380] in order to respond to recent calls for a more dynamic and processual understanding of networks. The concepts of potential and latent tie are deductively identified, and their implications for understanding how and why networks emerge, evolve, and change are explored. A longitudinal empirical study conducted with companies operating in the European motorsport industry reveals that firms take strategic actions to search for potential ties and reactivate latent ties in order to solve problems of network redundancy and overload. Examples are given, and their characteristics are examined to provide theoretical elaboration of the relationship between the types of tie and network evolution. These conceptual and empirical insights move understanding of the managerial challenge of building effective networks beyond static structural contingency models of optimal network forms to highlight the processes and capabilities of dynamic relationship building and network development. In so doing, this paper highlights the interrelationship between search and redundancy and the scope for strategic action alongside path dependence and structural influences on network processes.

Directional grain growth from anisotropic kinetic roughening of grain boundaries in sheared colloidal crystals

The fabrication of functional materials via grain growth engineering implicitly relies on altering the mobilities of grain boundaries (GBs) by applying external fields. Although computer simulations have alluded to kinetic roughening as a potential mechanism for modifying GB mobilities, its implications for grain growth have remained largely unexplored owing to difficulties in bridging the widely separated length and time scales. Here, by imaging GB particle dynamics as well as grain network evolution under shear, we present direct evidence for kinetic roughening of GBs and unravel its connection to grain growth in driven colloidal polycrystals. The capillary fluctuation method allows us to quantitatively extract shear-dependent effective mobilities. Remarkably, our experiments reveal that for sufficiently large strains, GBs with normals parallel to shear undergo preferential kinetic roughening, resulting in anisotropic enhancement of effective mobilities and hence directional grain growth. Single-particle level analysis shows that the mobility anisotropy emerges from strain-induced directional enhancement of activated particle hops normal to the GB plane. We expect our results to influence materials fabrication strategies for atomic and block copolymeric polycrystals as well.

Limits and tradeoffs in the control of autocatalytic systems

Despite the complexity of biological networks, we find that certain common architectures govern network structures. These architectures impose fundamental constraints on system performance and create tradeoffs that the system must balance in the face of uncertainty in the environment. This means that while a system may be optimized for a specific function through evolution, the optimal achievable state must follow these constraints. One such constraining architecture is autocatalysis, as seen in many biological networks including glycolysis and ribosomal protein synthesis. Using a minimal model, we show that ATP autocatalysis in glycolysis imposes stability and performance constraints and that the experimentally well-studied glycolytic oscillations are in fact a consequence of a tradeoff between error minimization and stability. We also show that additional complexity in the network results in increased robustness. Ribosome synthesis is also autocatalytic where ribosomes must be used to make more ribosomal proteins. When ribosomes have higher protein content, the autocatalysis is increased. We show that this autocatalysis destabilizes the system, slows down response, and also constrains the system’s performance. On a larger scale, transcriptional regulation of whole organisms also follows architectural constraints and this can be seen in the differences between bacterial and yeast transcription networks. We show that the degree distributions of bacterial transcription network follow a power law distribution while the yeast network follows an exponential distribution. We then explored the evolutionary models that have previously been proposed and show that neither the preferential linking model nor the duplication-divergence model of network evolution generates the power-law, hierarchical structure found in bacteria. However, in real biological systems, the generation of new nodes occurs through both duplication and horizontal gene transfers, and we show that a biologically reasonable combination of the two mechanisms generates the desired network.

Optimização de recursos para difusão em redes de próxima geração

Esta tese aborda o problema de optimização de recursos de rede, na entrega de Serviços de Comunicação em Grupo, em Redes de Próxima Geração que suportem tecnologias de difusão. De acordo com esta problemática, são feitas propostas que levam em atenção a evolução espectável das redes 3G em Redes Heterogéneas de Próxima Geração que incluam tecnologias de difusão tais como o DVB. A optimização de recursos em Comunicações em Grupo é apresentada como um desafio vertical que deve cruzar diversas camadas. As optimizações aqui propostas cobrem tanto a interface entre Aplicação e a Plataforma de Serviços para a disponibilização de serviços de comunicação em grupo, como as abstracções e mapeamentos feitos na interface entre a Rede Central e a Rede de Acesso Rádio. As optimizações propostas nesta tese, assumem que o caminho evolutivo na direcção de uma Rede de Próxima Geração é feito através do IP. Em primeiro lugar são endereçadas as optimizações entre a Aplicação e a Plataforma de Serviços que já podem ser integradas nas redes 3G existentes. Estas optimizações podem potenciar o desenvolvimento de novas e inovadoras aplicações, que através do uso de mecanismos de distribuição em difusão podem fazer um uso mais eficiente dos recursos de rede. De seguida são apresentadas optimizações ao nível da interface entre a Rede Central e a Rede de Acesso Rádio que abordam a heterogeneidade das redes futuras assim como a necessidade de suportar tecnologias de difusão. É ainda considerada a possibilidade de aumentar a qualidade de serviço de serviços de difusão através do mapeamento do IP multicast em portadoras unidireccionais. Por forma a validar todas estas optimizações, vários protótipos foram desenvolvidos com base num router avançado para redes de acesso de próxima geração. As funcionalidades e arquitectura de software desse router são também aqui apresentadas.

Bistability through triadic closure

We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics.

Evolutionary rewiring of bacterial regulatory networks

Bacteria have evolved complex regulatory networks that enable integration of multiple intracellular and extracellular signals to coordinate responses to environmental changes. However, our knowledge of how regulatory systems function and evolve is still relatively limited. There is often extensive homology between components of different networks, due to past cycles of gene duplication, divergence, and horizontal gene transfer, raising the possibility of cross-talk or redundancy. Consequently, evolutionary resilience is built into gene networks – homology between regulators can potentially allow rapid rescue of lost regulatory function across distant regions of the genome. In our recent study [Taylor, et al. Science (2015), 347(6225)] we find that mutations that facilitate cross-talk between pathways can contribute to gene network evolution, but that such mutations come with severe pleiotropic costs. Arising from this work are a number of questions surrounding how this phenomenon occurs.

A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model

Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.