Coupled dynamics of node and link states in complex networks: A model for language competition

Inspired by language competition processes, we present a model of coupled evolution of node and link states. In particular, we focus on the interplay between the use of a language and the preference or attitude of the speakers towards it, which we model, respectively, as a property of the interactions between speakers (a link state) and as a property of the speakers themselves (a node state). Furthermore, we restrict our attention to the case of two socially equivalent languages and to socially inspired network topologies based on a mechanism of triadic closure. As opposed to most of the previous literature, where language extinction is an inevitable outcome of the dynamics, we find a broad range of possible asymptotic configurations, which we classify as: frozen extinction states, frozen coexistence states, and dynamically trapped coexistence states. Moreover, metastable coexistence states with very long survival times and displaying a non-trivial dynamics are found to be abundant. Interestingly, a system size scaling analysis shows, on the one hand, that the probability of language extinction vanishes exponentially for increasing system sizes and, on the other hand, that the time scale of survival of the non-trivial dynamical metastable states increases linearly with the size of the system. Thus, non-trivial dynamical coexistence is the only possible outcome for large enough systems. Finally, we show how this coexistence is characterized by one of the languages becoming clearly predominant while the other one becomes increasingly confined to "ghetto-like" structures: small groups of bilingual speakers arranged in triangles, with a strong preference for the minority language, and using it for their intra-group interactions while they switch to the predominant language for communications with the rest of the population.Comment: 21 pages, 15 figure

Communities in temporal networks: from theoretical underpinnings to real-life applications

Static aggregations of network activity can unravel attributes of the complex systems they represent. However, they fall short when the structure of the systems changes over time. In some cases, changes are sluggish, such as in power grids, where lines enjoy a lengthy temporal permanence. In others, a high frequency of change is observed, such as on a network of online messages, social contacts, pathogen transmission or ball passing in a soccer game. In these cases, reducing what is inherently a temporal network to a static one, leads necessarily to a loss of information, such as causal relationships, precedence or reachability rules. Temporal networks are thus the main subject of this thesis, centered on the study of network evolution from the point of view of its clusters as significant meso-structures. The thesis has two interrelated parts. In the first, theoretical challenges are addressed and original algorithms, methods and tools are developed that can further the study of network theory. In the second, these developments are applied to the analysis of team invasion sports. A measurement of game dynamics was created based on a temporal network representation of a match, with nodes clustered by spatial proximity. These measurements were found to correlate with match events of known dynamics. Moreover, they reveal unique, multi-level, aspects of the game, from the individual players contributions, to the clusters of interacting players, to their teams and their matches, which is useful for game analysis, training and strategy development.As agregações estáticas das ligações de uma rede podem revelar atributos dos sistemas complexos que representam. Todavia, são insuficientes quando a estrutura dos sistemas se altera com o tempo. Em alguns casos, as transformações são lentas, tais como em redes de transmissão de eletricidade em que as linhas se mantêm inalteráveis por largos períodos de tempo. Noutras, regista-se uma taxa elevada de mudança, como por exemplo numa rede de mensagens em linha, contatos sociais, transmissão de patógenos ou passes num jogo de futebol. Nestes casos, reduzir o que é inerentemente uma rede temporal a uma rede estática, leva a uma perda de informação, tais como relações causais, regras de precedência ou de acessibilidade. Redes temporais são assim o tema desta tese, centrada nos seus agrupamentos, como meso-estruturas significantes. A tese está dividida em duas partes. Na primeira, são considerados problemas teóricos, e são desenvolvidos algoritmos, métodos e ferramentas que avançam o estudo da teoria de redes. Na segunda, estes desenvolvimentos são aplicados à análise de jogos desportivos coletivos de invasão. Foi criada uma medida de dinâmica do jogo, baseada na representação da partida através de uma rede temporal de nós agrupados por proximidade espacial. Os resultados obtidos correlacionam-se com eventos do jogo de dinâmica conhecida. Adicionalmente, esta medida revela aspetos únicos e multi-nível da dinâmica do jogo, desde a contribuição individual do jogador, até aos agrupamentos de jogadores, da equipa e das partidas, útil para a análise do jogo, de treino e de desenvolvimento estratégico

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

INFERRING SOCIAL NETWORKS FROM PASSIVELY COLLECTED WI-FI METADATA

The emergence of smartphones and other highly portable Wi-Fi enabled devices offers unprecedented amounts of information leaked through Wi-Fi metadata. The constantly connected nature of Wi-Fi devices together with the intimate relationship between users and their device presents an opportunity for using a user’s device to gain information about the user themselves. Through passive data collection, without interference or the possibility of being detected, it is possible to harvest large datasets. This work looks at the possibility of inferring underlying social networks through the analysis of these metadata traces. Using spatiotemporal proximity as an indicator of friendship, findings demonstrate the ability to accurately predict underlying social structures in various simulated settings

The "New" Science of Networks

In recent years, the analysis and modeling of networks, and also networked dynamical systems, have been the subject of considerable interdisciplinary interest, yielding several hundred papers in physics, mathematics, computer science, biology, economics, and sociology journals (Newman 2003c), as well as a number of books (Barabasi 2002, Buchanan 2002,Watts 2003). Here I review the major findings of this emerging field and discuss briefly their relationship with previous work in the social and mathematical sciences