Redes complexas são utilizadas para representar sistemas complexos, compostos de elementos que interagem uns com os outros. Uma das grandes vantagens de se empregar as redes é a possibilidade de se estudar a topologia presente nos mais diversos sistemas para obtermos informações sobre eles, entendê-los e compará-los. Devido à sua importância para a compreensão de processos intracelulares, desde início do desenvolvimento da área das redes complexas estudou-se a topologia da interação entre proteínas. Entretanto nos últimos anos com o desenvolvimento de novas técnicas de detecção o número de proteínas e interações reportadas cresceu de maneira muito acentuada; além disso, também existem alguns pontos sobre a sua topologia sobre os quais ainda não existe um consenso, como por exemplo qual a distribuição de graus desse tipo de rede. Neste trabalho estudamos as propriedades topológicas de redes de interação entre proteínas, utilizando as informações do banco de dados STRING, com ênfase no comportamento de suas medidas de centralidade e do espectro da matriz Laplaciana normalizada. Tanto a análise das medidas de centralidade e de suas correlações, quanto do espectro da matriz Laplaciana mostram que existem padrões topológicos que são conservados entre as redes dos organismos e que os mesmos também podem ser empregados para sua caracterização. Nossos resultados também mostram que as funções biológicas desempenhadas pelas proteínas podem ser identificadas pelas medidas de centralidade. Especificamente para a centralidade de autovetor, nossas análises indicam que ela está localizada nos maiores K-cores das redes consideradas. Os resultados aqui obtidos ressaltam que muitas informações relevantes podem ser extraídas da topologia das interações entre proteínas, além de indicarem a existência de possíveis estruturas conservadas; entretanto devido a incompletude dessas redes mais estudos precisam ser conduzidos para a avaliação de possíveis mudanças nos resultados aqui apresentados. / Complex networks can be used to model complex systems, composed of main elements that interact with each other. The advantage of using this approach is the possibility to study the topology of a wide range of systems so that we can get more information, understand and compare them. Due to its importance on the understanding of the intracellular biological processes, since the early beginning of the development of the complex networks field protein-protein interaction topologies have been studied. However, new techniques for the detection of proteins and their interactions have been developed recently, which has significantly increased the availability and reliability of the corresponding data over the last few years; moreover, there still are some debate about the topology of protein-protein interaction networks such as the degree distribution of this type of network. Here we will study the topological properties of protein-protein interaction networks created using the information of the STRING database focusing on centrality measures of their nodes, the correlation between them, and the normalized Laplacian matrix spectrum. Our results show the existence of topological patterns conserved between the protein interaction networks of different organisms and that both the correlation of the centrality pairs and the spectrum of the Laplacian matrix can be used for network characterization. Another study indicates that the set of centrality measures of a protein can be used to identify clusters with well defined biological functions. A more detailed look at the eigenvector centrality behavior reveals that this measure is localized on the proteins of the highest k-cores for all networks. These results highlight the importance of the topology on the study of protein-protein interactions and that more studies can lead to a better a more complete understanding of such systems.

Respondent-driven sampling (RDS) is very useful in collecting data from individuals in hidden populations, where a sampling frame does not exist. It starts with researchers choosing initial respondents from a group which may be involved in taboo or illegal activities, after which they recruit other peers who belong to the same group. Analysis results in unbiased estimates of population proportions though with strong assumptions about the underlying social network and RDS recruitment process. These assumptions bear little resemblance to reality, and thus compromise the estimation of any means, population proportions or variances inferred from studies. The topology of the contact network, denoted by the number of links each person has, provides insight into the processes of infectious disease spread. The overall objective of the thesis is to identify the topology of an injection drug use network, and critically review the methods developed to produce estimates. The topology of people who inject drugs (PWID) collected by RDS in Ottawa, 2006 was compared with a Poisson distribution, an exponential distribution, a power-law distribution, and a lognormal distribution. The contact distribution was then evaluated against a small-world network characterized by high clustering and low average distances between individuals. Last a systematic review of the methods used to produce RDS mean and variance estimates was conducted. The Poisson distribution, a type of random distribution, was not an appropriate fit for PWID network. However, the PWID network can be classified as a small world network organised with many connections and short distances between people. Prevention of transmission in such networks should be focussed on the most active people (clustered individuals and hubs) as intervention with any others is less effective. The systematic review contained 32 articles which included the development and evaluation of 12 RDS mean and 6 variance estimators. Overall, the majority of estimators perform roughly the same, with the exception of RDSIEGO which outperformed the 6 other RDS mean estimators. The Tree bootstrap variance estimate does not rely on modelling RDS as a first order Markov (FOM) process, which seems to be the main limitation of the other existing estimators. The lack of FOM as an assumption and the flexible application of this variance estimator to any RDS point estimate make the Tree bootstrapping estimator a more efficient choice.

<p>A complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.</p>

In order to meet the availability requirements of modern communication networks, a number of survivability techniques were developed that adapt the demand-wise shared protection design model to incorporate strategies increasing network availability. The survivability methodologies developed took two approaches. The first incorporated availability directly into the network design model. The second ensured minimum dual failure restorability was set within the model. These methodologies were developed for predetermined topologies, as well as to have topology optimization incorporated into the model. All methodologies were implemented and analyzed on a set of samples. The analysis examined cost, topology and actual availability of the network designs. Availability design was effective but computationally intensive and difficult to design. Minimum dual failure restorability was also effective in increasing availability with a significant caveat, dual failure restorability increased exposure to possible failures, and without sufficient levels of dual failure restorability could have a negative impact on availability. / Engineering Management

A complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.

The whole in a complex system is the sum of its parts, plus the interactions between the parts. Understanding social, biological, and economic systems therefore often depends on understanding their patterns of interactions---their networks. In this thesis, the approach is to understand complex systems by making simple network models with nodes and links. It is first of all an attempt to investigate how the communication over the network affects the network structure and, vice versa, how the network structure affects the conditions for communication. To explore the local mechanism behind network organization, we used simplified social systems and modeled the response to communication. Low communication levels resulted in random networks, whereas higher communication levels led to structured networks with most nodes having very few links and a few nodes having very many links. We also explored various models where nodes merge into bigger units, to reduce communication costs, and showed that these merging models give rise to the same kind of structured networks. In addition to this modeling of communication networks, we developed new ways to measure and characterize real-world networks. For example, we found that they in general favor communication on short distance, two-three steps away in the network, within what we call the information horizon. / Helheten i ett komplext system är mer än summan av dess delar, då den även inbegriper interaktionerna mellan dem. Att studera sociala, biologiska och ekonomiska system blir därför ofta en fråga om att förstå deras interaktionsmönster, d.v.s. deras nätverk av noder och länkar. Med utgångspunkt i enkla nätverksmodeller undersöker avhandlingen i huvudsak hur kommunikation i nätverk påverkar nätverksstrukturen och, vice versa, hur nätverksstrukturen påverkar villkoren för kommunikation. Vi utforskade mekanismerna bakom hur nätverk är organiserade genom att modellera effekten av kommunikation i förenklade sociala system. En låg kommunikationsnivå visade sig ge upphov till kaotiska nätverk där ingen nod i princip hade fler länkar än någon annan. En hög kommunikationsnivå resulterade däremot i strukturerade nätverk, med några få centrala noder med många länkar, medan flertalet noder var perifera med enbart några få länkar. Det visade sig också att alla aktörer i nätverket gynnades av kommunikation, även när den var ojämnt fördelad. Kvaliteten på kommunikationen, d.v.s. informationens giltighet, var också avgörande för vilka positioner som gynnades i ett nätverk, vilket vi visade genom att studera aktörer som spred falsk information. Eftersom effektiv kommunikation är en viktig del i många nätverk betraktar vi utvecklingen av dem som en optimeringsprocess. Varje kommunikationshandling mellan noderna tar tid och genom att slå sig samman till större enheter begränsas dessa kostnader och gör nätverket effektivare. Dessa s.k. sammanslagningsmodeller gav upphov till samma typ av strukturerade nätverk som ovan. Genom att utveckla olika sätt att mäta nätverksstrukturer visade vi bland annat att många verkliga system främjar kommunikation över korta avstånd, två-tre steg bort i nätverket, innanför det vi kallar informationshorisonten. Vi uppskattade också den mängd information som krävs för att orientera sig i städer, och fann att det är lättare att hitta i moderna, planerade städer än i äldre städer som utvecklats under lång tid.

State estimation plays an important role in modern power system energy management systems. The network observability is a pre-requisite for the state estimation solution. Topological error in the network may cause the state estimation results to be seriously biased. This dissertation studies new schemes to improve the conventional state estimation in the above aspects. A new algorithm for cost minimization in the measurement placement design is proposed in this dissertation. The new algorithm reduces the cost of measurement installation and retains the network observability. Two levels of measurement place- ment designs are obtained: the basic level design guarantees the whole network to be observable using only the voltage magnitude measurement and the branch power flow measurements. The advanced level design keeps the network observable under certain contingencies. To preserve as many substation measurements as possible and maintain the net-work observability, an advanced network topology processor is introduced. A new method - the dynamic utilization of substation measurements (DUSM) - is presented. Instead of seeking the installation of new measurements in the system, this method dynamically calculates state estimation measurement values by applying the current law that regulates different measurement values implicitly. Its processing is at the substation level and, therefore, can be implemented independently in substations. This dissertation also presents a new way to verify circuit breaker status and identify topological errors. The new method improves topological error detection using the method of DUSM. It can be seen that without modifying the state estimator, the status of a circuit breaker may still be verified even without direct power flow measurements. Inferred measurements, calculated by DUSM, are used to help decide the CB status. To reduce future software code maintenance and to provide standard data ex- changes, the newly developed functions were developed in Java, with XML format input/output support. The effectiveness and applicability of these functions are ver-ified by various test cases.

A connectivity adjusting learning algorithm, Optimal Brain Growth (OBG) was proposed. Contrast to the conventional training methods for the Artificial Neural Network (ANN) which focus on the weight-only optimization, the OBG method trains both weights and connectivity of a network in a single training process. The standard Back-Propagation (BP) algorithm was extended to exploit the error gradient information of the latent connection whose current weight has zero value. Based on this, the OBG algorithm makes a rational decision between a further adjustment of an existing connection weight and a creation of a new connection having zero weight. The training efficiency of a growing network is maintained by freezing stabilized connections in the further optimization process. A stabilized computational unit is also decomposed into two units and a particular set of decomposition rules guarantees a seamless local re-initialization of a training trajectory. The OBG method was tested for the multiple canonical, regression and classification problems and for a surrogate modeling of the pressure distribution on transonic airfoils. The OBG method showed an improved learning capability in computationally efficient manner compared to the conventional weight-only training using connectivity-fixed Multilayer Perceptrons (MLPs).

<p>This report concerns the implementation of a simulation framework to evaluate an emerging peer-to-peer network topology scheme using 8-point hypercircles, entitled HyperCircle. This topology was proposed in order to alleviate some of the drawbacks of current P2P systems evolving in an uncontrolled manner, such as scalability issues, network overload and long search times. The framework is supposed to be used to evaluate the advantages of this new topology. The framework has been built on top of an existing simulator software solution, the selection of which was an important part of the development. Weighing different variables such as scalability and API usability, the selection fell on OverSim, an open-source discreet-event simulator based on OMNET++.</p><p>After formalizing the protocol for easier implementation, as well as extending it for better performance, implementation followed using C++ with OverSim’s API and simulation library. Implemented as a module (alongside other stock modules providing their own protocols such as Chord and Kademlia), it can be used in OverSim to simulate a user-defined network using one of the simulation routine applications provided (or using a custom application written by the user). For the purposes of this thesis, the standard application KBRTestApp was used; an application sending test messages between randomly selected nodes, while adding and removing nodes at specific time intervals. The adding and removing of nodes can be configured with probability parameters.</p><p>Tentative testing shows that this implementation of the HyperCircle protocol has a certain performance gain over the OverSim implementations of the Chord and Kademlia protocols, measurable in the time it takes a message to get from sender to recipient. Further testing is outside the scope of this thesis.</p>

Not available / text