564,089 research outputs found
Hierarchical Models for Independence Structures of Networks
We introduce a new family of network models, called hierarchical network
models, that allow us to represent in an explicit manner the stochastic
dependence among the dyads (random ties) of the network. In particular, each
member of this family can be associated with a graphical model defining
conditional independence clauses among the dyads of the network, called the
dependency graph. Every network model with dyadic independence assumption can
be generalized to construct members of this new family. Using this new
framework, we generalize the Erd\"os-R\'enyi and beta-models to create
hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for
parameter estimation as well as simulation studies for models with sparse
dependency graphs.Comment: 19 pages, 7 figure
Traveling and pinned fronts in bistable reaction-diffusion systems on network
Traveling fronts and stationary localized patterns in bistable
reaction-diffusion systems have been broadly studied for classical continuous
media and regular lattices. Analogs of such non-equilibrium patterns are also
possible in networks. Here, we consider traveling and stationary patterns in
bistable one-component systems on random Erd\"os-R\'enyi, scale-free and
hierarchical tree networks. As revealed through numerical simulations,
traveling fronts exist in network-organized systems. They represent waves of
transition from one stable state into another, spreading over the entire
network. The fronts can furthermore be pinned, thus forming stationary
structures. While pinning of fronts has previously been considered for chains
of diffusively coupled bistable elements, the network architecture brings about
significant differences. An important role is played by the degree (the number
of connections) of a node. For regular trees with a fixed branching factor, the
pinning conditions are analytically determined. For large Erd\"os-R\'enyi and
scale-free networks, the mean-field theory for stationary patterns is
constructed
Improving controllability of complex networks by rewiring links regularly
Network science have constantly been in the focus of research for the last
decade, with considerable advances in the controllability of their structural.
However, much less effort has been devoted to study that how to improve the
controllability of complex networks. In this paper, a new algorithm is proposed
to improve the controllability of complex networks by rewiring links regularly
which transforms the network structure. Then it is demonstrated that our
algorithm is very effective after numerical simulation experiment on typical
network models (Erd\"os-R\'enyi and scale-free network). We find that our
algorithm is mainly determined by the average degree and positive correlation
of in-degree and out-degree of network and it has nothing to do with the
network size. Furthermore, we analyze and discuss the correlation between
controllability of complex networks and degree distribution index: power-law
exponent and heterogeneit
Neighborhood properties of complex networks
A concept of neighborhood in complex networks is addressed based on the
criterion of the minimal number os steps to reach other vertices. This amounts
to, starting from a given network , generating a family of networks
such that, the vertices that are steps apart in
the original , are only 1 step apart in . The higher order
networks are generated using Boolean operations among the adjacency matrices
that represent . The families originated by the well known
linear and the Erd\"os-Renyi networks are found to be invariant, in the sense
that the spectra of are the same, up to finite size effects. A further
family originated from small world network is identified
Role based behavior analysis
Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de CiĂŞncias, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso sĂŁo trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de InformacĂŁo/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nĂvel da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. AlĂ©m disso, se nĂŁo houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiĂŞncias noutras. Finalmente, incentivar a proactividade nĂŁo implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese Ă© desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo Ă© iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com caracterĂsticas semelhantes sĂŁo agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais sĂŁo comprados com os perfis de grupo, e os que diferem significativamente sĂŁo marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda mĂ©todos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisĂŁo comportamento semelhante. AlĂ©m disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos sĂŁo assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it
How motifs condition critical thresholds for tipping cascades in complex networks: Linking Micro- to Macro-scales
In this study, we investigate how specific micro interaction structures
(motifs) affect the occurrence of tipping cascades on networks of stylized
tipping elements. We compare the properties of cascades in Erd\"os-R\'enyi
networks and an exemplary moisture recycling network of the Amazon rainforest.
Within these networks, decisive small-scale motifs are the feed forward loop,
the secondary feed forward loop, the zero loop and the neighboring loop.
Of all motifs, the feed forward loop motif stands out in tipping cascades
since it decreases the critical coupling strength necessary to initiate a
cascade more than the other motifs. We find that for this motif, the reduction
of critical coupling strength is 11% less than the critical coupling of a pair
of tipping elements. For highly connected networks, our analysis reveals that
coupled feed forward loops coincide with a strong 90% decrease of the critical
coupling strength.
For the highly clustered moisture recycling network in the Amazon, we observe
regions of very high motif occurrence for each of the four investigated motifs
suggesting that these regions are more vulnerable. The occurrence of motifs is
found to be one order of magnitude higher than in a random Erd\"os-R\'enyi
network.
This emphasizes the importance of local interaction structures for the
emergence of global cascades and the stability of the network as a whole
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