Dense subgraphs on dynamic networks

In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense substructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that (2 + e)-approximate the densest subgraph and (3 + e)-approximate the at-least-k-densest subgraph (for a given parameter k). Our algorithms work for a wide range of parameter values and run in O(D log n) time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and at-least-k-densest subgraph problems for static distributed graphs. © 2012 Springer-Verlag.

Algorithms for Self-Healing Networks

Many modern networks are \emph{reconfigurable}, in the sense that the topology of the network can be changed by the nodes in the network. For example, peer-to-peer, wireless and ad-hoc networks are reconfigurable. More generally, many social networks, such as a company's organizational chart; infrastructure networks, such as an airline's transportation network; and biological networks, such as the human brain, are also reconfigurable. Modern reconfigurable networks have a complexity unprecedented in the history of engineering, resembling more a dynamic and evolving living animal rather than a structure of steel designed from a blueprint. Unfortunately, our mathematical and algorithmic tools have not yet developed enough to handle this complexity and fully exploit the flexibility of these networks. We believe that it is no longer possible to build networks that are scalable and never have node failures. Instead, these networks should be able to admit small, and maybe, periodic failures and still recover like skin heals from a cut. This process, where the network can recover itself by maintaining key invariants in response to attack by a powerful adversary is what we call \emph{self-healing}. Here, we present several fast and provably good distributed algorithms for self-healing in reconfigurable dynamic networks. Each of these algorithms have different properties, a different set of gaurantees and limitations. We also discuss future directions and theoretical questions we would like to answer. %in the final dissertation that this document is proposed to lead to.

Efficient Network in Player and Partner Heterogeneity Models

This paper characterizes efficient networks in player and partner heterogeneity models for both the one-way flow and the two-way flow models. Player (partner) dependent network formation allows benefits and costs to be player (partner) heterogeneous which is an important extension for modeling social networks in the real world. Employing widely used assumptions, I show that efficient networks in the two way flow model are minimally connected and have star or derivative of star type architectures, whereas efficient networks in the one way flow model have wheel architectures.

Effects of peer network interactions on adolescent cannabis use

Purpose
This study capitalises on three waves of longitudinal data from a cohort of 4351 secondary school pupils to examine the effects on individuals’ cannabis use uptake of both peer cannabis use and position within a peer network.

Design/methodology/approach
Both cross-sectional and individual fixed effects models are used to estimate the effect on cannabis use of nominated friends’ cannabis use, of reciprocity and transitivity of nominations across the friendship cluster, and of interactions between these nominated friends. Post hoc analyses parsed the behaviour of reciprocating and non-reciprocating friends.

Findings
Cannabis use varied depending on the stability of friendship network and the degree of reciprocity and interconnectedness within the group. Behavioural influence was strong, but interaction effects were observed between the prevalence of cannabis use among friends, the structure of the friendship group and ego’s proximity to group members. These interactions demonstrate that behavioural influence is more salient in more cohesive groups. When reciprocating and non-reciprocating friends’ mean cannabis use were separated, influence from reciprocating friends was estimated at twice the magnitude of other friends.

Originality/value
While preventing any one individual from using cannabis is likely to have a multiplier effect on classmates, the bonds and interactions between classmates will determine which classmates are affected by this multiplier and the salience of that effect.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Networked tourism: from world destinations to inter-organizations

Na última década, a referência ao conceito de redes cresceu rapidamente entre a literatura sobre turismo, geralmente aplicado a tópicos como as interorganizações, estrutura de multi-destinos, espaços de Turismo online, entre outros. O conceito de rede difundiu-se na natureza e na sociedade, em áreas que vão desde a Biologia à Medicina, ou da Economia à Gestão, e o conhecimento sobre redes tem vindo a impulsionar uma teoria comum para facilitar a compreensão de diferentes sistemas complexos e a representação das ligações entre organizações, acções, bens, proteínas ou pessoas. A tese teve como propósito o encontro de um eixo comum entre dois campos férteis de investigação através de uma revisão teórica sistemática. A investigação sobre redes complexas é um campo recente na Física que tem vindo a desenvolver-se bastante na última década com fortes aplicações interdisciplinares. Por outro lado, a análise de redes sociais é uma área de investigação activa em Sociologia e Economia há bastante tempo. O estudo das implicações das redes complexas para a ciência das redes de turismo é uma área promissora já com resultados fascinantes. A tese tem três resultados principais. Primeiro, traz conhecimento das ricas áreas de conhecimento sobre redes complexas e redes sociais. Em segundo lugar, apresenta modelos evolutivos que melhor se adaptam às chegadas turísticas internacionais. Como se organizam as redes sociais? Como é que os indivíduos escolhem os seus destinos de viagem? Estes são exemplos de questões que serão abordadas na tese. Em terceiro lugar, discute resultados que fazem notar comportamentos comuns entre redes em turismo e outras redes reais. O que é comum a todas as redes na natureza? Adicionalmente, os padrões encontrados entre os destinos turísticos mostram um comportamento não social, com destinos mais característicos de redes económicas e sistemas tecnológicos que questionam a faceta social do sector do turismo. Por acréscimo, a rede de transportes aéreos e a rede de turismo mostram diferenças consideráveis que se podem dever a razões políticas ou outras que provavelmente explicam o aumento da utilização de voos charters.

Methodologies for traffic profiling in communication networks

Internet Tra c, Internet Applications, Internet Attacks, Tra c Pro ling, Multi-Scale Analysis abstract Nowadays, the Internet can be seen as an ever-changing platform where new and di erent types of services and applications are constantly emerging. In fact, many of the existing dominant applications, such as social networks, have appeared recently, being rapidly adopted by the user community. All these new applications required the implementation of novel communication protocols that present di erent network requirements, according to the service they deploy. All this diversity and novelty has lead to an increasing need of accurately pro ling Internet users, by mapping their tra c to the originating application, in order to improve many network management tasks such as resources optimization, network performance, service personalization and security. However, accurately mapping tra c to its originating application is a di cult task due to the inherent complexity of existing network protocols and to several restrictions that prevent the analysis of the contents of the generated tra c. In fact, many technologies, such as tra c encryption, are widely deployed to assure and protect the con dentiality and integrity of communications over the Internet. On the other hand, many legal constraints also forbid the analysis of the clients' tra c in order to protect their con dentiality and privacy. Consequently, novel tra c discrimination methodologies are necessary for an accurate tra c classi cation and user pro ling. This thesis proposes several identi cation methodologies for an accurate Internet tra c pro ling while coping with the di erent mentioned restrictions and with the existing encryption techniques. By analyzing the several frequency components present in the captured tra c and inferring the presence of the di erent network and user related events, the proposed approaches are able to create a pro le for each one of the analyzed Internet applications. The use of several probabilistic models will allow the accurate association of the analyzed tra c to the corresponding application. Several enhancements will also be proposed in order to allow the identi cation of hidden illicit patterns and the real-time classi cation of captured tra c. In addition, a new network management paradigm for wired and wireless networks will be proposed. The analysis of the layer 2 tra c metrics and the di erent frequency components that are present in the captured tra c allows an e cient user pro ling in terms of the used web-application. Finally, some usage scenarios for these methodologies will be presented and discussed.

Novel critical phenomena in optimization driven processes on networks

The work presented in this Ph.D thesis was developed in the context of complex network theory, from a statistical physics standpoint. We examine two distinct problems in this research field, taking a special interest in their respective critical properties. In both cases, the emergence of criticality is driven by a local optimization dynamics. Firstly, a recently introduced class of percolation problems that attracted a significant amount of attention from the scientific community, and was quickly followed up by an abundance of other works. Percolation transitions were believed to be continuous, until, recently, an 'explosive' percolation problem was reported to undergo a discontinuous transition, in [93]. The system's evolution is driven by a metropolis-like algorithm, apparently producing a discontinuous jump on the giant component's size at the percolation threshold. This finding was subsequently supported by number of other experimental studies [96, 97, 98, 99, 100, 101]. However, in [1] we have proved that the explosive percolation transition is actually continuous. The discontinuity which was observed in the evolution of the giant component's relative size is explained by the unusual smallness of the corresponding critical exponent, combined with the finiteness of the systems considered in experiments. Therefore, the size of the jump vanishes as the system's size goes to infinity. Additionally, we provide the complete theoretical description of the critical properties for a generalized version of the explosive percolation model [2], as well as a method [3] for a precise calculation of percolation's critical properties from numerical data (useful when exact results are not available). Secondly, we study a network flow optimization model, where the dynamics consists of consecutive mergings and splittings of currents flowing in the network. The current conservation constraint does not impose any particular criterion for the split of current among channels outgoing nodes, allowing us to introduce an asymmetrical rule, observed in several real systems. We solved analytically the dynamic equations describing this model in the high and low current regimes. The solutions found are compared with numerical results, for the two regimes, showing an excellent agreement. Surprisingly, in the low current regime, this model exhibits some features usually associated with continuous phase transitions.

IP/optical integration in access network infrastructures: key issues on resource provisioning

This thesis contributes to the advancement of Fiber-Wireless (FiWi) access technologies, through the development of algorithms for resource allocation and energy efficient routing. FiWi access networks use both optical and wireless/cellular technologies to provide high bandwidth and ubiquity, required by users and current high demanding services. FiWi access technologies are divided in two parts. In one of the parts, fiber is brought from the central office to near the users, while in the other part wireless routers or base stations take over and provide Internet access to users. Many technologies can be used at both the optical and wireless parts, which lead to different integration and optimization problems to be solved. In this thesis, the focus will be on FiWi access networks that use a passive optical network at the optical section and a wireless mesh network at the wireless section. In such networks, two important aspects that influence network performance are: allocation of resources and traffic routing throughout the mesh section. In this thesis, both problems are addressed. A fair bandwidth allocation algorithm is developed, which provides fairness in terms of bandwidth and in terms of experienced delays among all users. As for routing, an energy efficient routing algorithm is proposed that optimizes sleeping and productive periods throughout the wireless and optical sections. To develop the stated algorithms, game theory and networks formation theory were used. These are powerful mathematical tools that can be used to solve problems involving agents with conflicting interests. Since, usually, these tools are not common knowledge, a brief survey on game theory and network formation theory is provided to explain the concepts that are used throughout the thesis. As such, this thesis also serves as a showcase on the use of game theory and network formation theory to develop new algorithms.

Personal networks in portuguese society : a configurational and lifecourse approach

Tese de doutoramento, Sociologia (Sociologia da Família, da Juventude e das Relações de Género), Universidade de Lisboa, Instituto de Ciências Sociais, 2014

A utilização do facebook como ferramenta de ensino colaborativo numa turma de 11º ano do ensino profissional

Relatório da Prática de Ensino Supervisionada, (Mestrado em Ensino da Economia e da Contabilidade), Universidade de Lisboa, 2014

Mining social individuals movements and friends based on mobile devices

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

A influência do modelo de aceitação da tecnologia na utilização da rede social Facebook na perspetiva do perfil pessoal

Dissertação de Mestrado apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação do Mestre José Duarte Santos

Migrant networks and migrant intentions to return

Social ties are potentially an important determinant of migrants’ intention to return to their home country, and yet this topic has not been addressed in the existing economics literature on international migration. This study examines the absolute and relative importance of migrant social networks both at destination and at origin. We base our research on experimental data from Batista and Narciso (2013)1. By defining networks according to different characteristics of their members and migrant return intentions with respect to three different time horizons, we are able to dissect the network effect into its components. After controlling for unobserved heterogeneity and reverse causality biases we find that network at home seems to be the most important determinant of the migrant’s intention to return home within five and ten years.