A new design principle of robust onion-like networks self-organized in growth

Today's economy, production activity, and our life are sustained by social and technological network infrastructures, while new threats of network attacks by destructing loops have been found recently in network science. We inversely take into account the weakness, and propose a new design principle for incrementally growing robust networks. The networks are self-organized by enhancing interwoven long loops. In particular, we consider the range-limited approximation of linking by intermediations in a few hops, and show the strong robustness in the growth without degrading efficiency of paths. Moreover, we demonstrate that the tolerance of connectivity is reformable even from extremely vulnerable real networks according to our proposed growing process with some investment. These results may indicate a prospective direction to the future growth of our network infrastructures.Comment: 21 pages, 10 figures, 1 tabl

Relational and Attributive Activity in Virtual Communities

Although Virtual Communities (ViCs) have been considered as an important e-commerce instrument, little is known about their evolution and the changes in the communication activity of its users. A frequent finding is that a small number of the participants is responsible for the majority of messages and, in contrast, a large portion of the users only write one or a few message(s). This discrepancy deserves more attention. In this paper, the heterogeneity of the communication activity is examined longitudinally on the basis of the graph-theoretical models "Random Network Theory" and "Scale-free Networks". The fusion of these two models allows operationalization of the heterogeneity of the relational as well as the attributive communication activity in ViCs. The adjustment of the empirical distribution functions of the examined ViCs to this model shows a predominance of preferential over uniform binding. This "rich get richer phenomenon" proves temporally stable and leads to the emergence of heterogeneity of the member's activities. We conclude that instead of stimulating all participants, it appears to be more promising to address the lead users as a main target. Their attachments to other users may be utilized in a positive and amplifying way in order increase a community's communication activity

Trust based attachment

In social systems subject to indirect reciprocity, a positive reputation is key for increasing one's likelihood of future positive interactions. The flow of gossip can amplify the impact of a person's actions on their reputation depending on how widely it spreads across the social network, which leads to a percolation problem. To quantify this notion, we calculate the expected number of individuals, the "audience", who find out about a particular interaction. For a potential donor, a larger audience constitutes higher reputational stakes, and thus a higher incentive, to perform "good" actions in line with current social norms. For a receiver, a larger audience therefore increases the trust that the partner will be cooperative. This idea can be used for an algorithm that generates social networks, which we call trust based attachment (TBA). TBA produces graphs that share crucial quantitative properties with real-world networks, such as high clustering, small-world behavior, and power law degree distributions. We also show that TBA can be approximated by simple friend-of-friend routines based on triadic closure, which are known to be highly effective at generating realistic social network structures. Therefore, our work provides a new justification for triadic closure in social contexts based on notions of trust, gossip, and social information spread. These factors are thus identified as potential significant influences on how humans form social ties

Emerging Cooperation in N-Person Iterated Prisoner's Dilemma over Dynamic Complex Networks

The N-Person Iterated Prisoner's Dilemma (NIPD) is an interesting game that has proved to be very useful to explore the emergence of cooperation in multi-player scenarios. Within this game, the way that agents are interconnected is a key element that influences cooperation. In this context, complex networks provide a realistic model of the topological features found in Nature and in many social and technological networks. Considering these networks, it is interesting to study the network evolution, given the possibility that agents can change their neighbors (dynamic rewire), when non-cooperative behaviors are detected. In this paper, we present a model of the NIPD game where a population of genetically-coded agents compete altogether. We analyze how different game parameters, and the network topology, affect the emergence of cooperation in static complex networks. Based on that, we present the main contribution of the paper that concerns the influence of dynamic rewiring in the emergence of cooperation over the NIPD

Evolution of Prosocial Behavior through Preferential Detachment and Its Implications for Morality.

The current project introduces a general theory and supporting models that offer a plausible explanation and viable mechanism for generating and perpetuating prosocial behavior. The proposed mechanism is preferential detachment and the theory proposed is that agents utilizing preferential detachment will sort themselves into social arrangements such that the agents who contribute a benefit to the members of their group also do better for themselves in the long run. Agents can do this with minimal information about their environment, the other agents, the future, and with minimal cognitive/computational ability. The conclusion is that self-organizing into groups that maintain prosocial behaviors may be simpler and more robust than previously thought. The primary contribution of this research is that a single, simple mechanism operating in different contexts generates the conceptually distinct prosocial behaviors achieved by other models, and in a manner that is more amenable to evolutionary explanations. It also bears importantly on explanations of the evolution of our moral experiences and their connection with prosociality.Ph.D.Political Science and PhilosophyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91448/1/bramson_1.pd

Complex networks analysis in team sports performance: multilevel hypernetworks approach to soccer matches

Humans need to interact socially with others and the environment. These interactions lead to complex systems that elude naïve and casuistic tools for understand these explanations. One way is to search for mechanisms and patterns of behavior in our activities. In this thesis, we focused on players’ interactions in team sports performance and how using complex systems tools, notably complex networks theory and tools, can contribute to Performance Analysis. We began by exploring Network Theory, specifically Social Network Analysis (SNA), first applied to Volleyball (experimental study) and then on soccer (2014 World Cup). The achievements with SNA proved limited in relevant scenarios (e.g., dynamics of networks on n-ary interactions) and we moved to other theories and tools from complex networks in order to tap into the dynamics on/off networks. In our state-of-the-art and review paper we took an important step to move from SNA to Complex Networks Analysis theories and tools, such as Hypernetworks Theory and their structural Multilevel analysis. The method paper explored the Multilevel Hypernetworks Approach to Performance Analysis in soccer matches (English Premier League 2010-11) considering n-ary cooperation and competition interactions between sets of players in different levels of analysis. We presented at an international conference the mathematical formalisms that can express the players’ relationships and the statistical distributions of the occurrence of the sets and their ranks, identifying power law statistical distributions regularities and design (found in some particular exceptions), influenced by coaches’ pre-match arrangement and soccer rules.Os humanos necessitam interagir socialmente com os outros e com o envolvimento. Essas interações estão na origem de sistemas complexos cujo entendimento não é captado através de ferramentas ingénuas e casuísticas. Uma forma será procurar mecanismos e padrões de comportamento nas atividades. Nesta tese, o foco centra-se na utilização de ferramentas dos sistemas complexos, particularmente no contributo da teoria e ferramentas de redes complexas, na Análise do Desempenho Desportivo baseado nas interações dos jogadores de equipas desportivas. Começámos por explorar a Teoria das Redes, especificamente a Análise de Redes Sociais (ARS) no Voleibol (estudo experimental) e depois no futebol (Campeonato do Mundo de 2014). As aplicações da ARS mostraram-se limitadas (por exemplo, na dinâmica das redes em interações n-árias) o que nos trouxe a outras teorias e ferramentas das redes complexas. No capítulo do estadoda- arte e artigo de revisão publicado, abordámos as vantagens de utilização de outras teorias e ferramentas, como a análise Multinível e Teoria das Híperredes. No artigo de métodos, apresentámos a Abordagem de Híperredes Multinível na Análise do Desempenho em jogos de futebol (Premier League Inglesa 2010-11) considerando as interações de cooperação e competição nos conjuntos de jogadores, em diferentes níveis de análise. Numa conferência internacional, apresentámos os formalismos matemáticos que podem expressar as relações dos jogadores e as distribuições estatísticas da ocorrência dos conjuntos e a sua ordem, identificando regularidades de distribuições estatísticas de power law e design (encontrado nalgumas exceções estatísticas específicas), promovidas pelos treinadores na preparação dos jogos e constrangidas pelas regras do futebol