Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.

Abstract: The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.

Applications of weighted association networks applied to compositional data in biology.

Abstract: Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Leveraging Network Theory and Stress Tests to Assess Interdependencies in Critical Infrastructures

Abstract: Many modern critical infrastructures manifest reciprocal dependencies at various levels and on a time-evolving scale. Network theory has been exploited in the last decades to achieve a better understanding of topologies, correlations and propagation paths in case of perturbations. The discipline is providing interesting insights into aspects such as fragility and robustness of different network layouts against various types of threats, despite the difficulties arising in the modeling of the associated processes and entity relationships. Indeed, the evolution of infrastructures is not, in general, the straightforward outcome of a comprehensive a priori design. Rather, factors such as societal priorities, technical and budgetary constraints, critical events and the quest for better and cost-effective services induce a continuous change, while new kinds of interdependencies emerge. As a consequence, mapping emerging behavior can constitute a challenge and promote the development of innovative approaches to analysis and management. Among them, stress tests are entering the stage in order to assess networked infrastructures and reveal the associated operational boundaries and risk exposures. In this chapter, we first overview key developments of network science and its applications to primary infrastructure sectors. Secondly, we address the implementation of network-theoretical concepts in actions related to resilience enhancement, referring in particular to the case of stress tests in the banking sector. Finally, a discussion on the relevance of those concepts to critical infrastructure governance is provided.

Power indices of influence games and new centrality measures for social networks

Abstract: In social network analysis, there is a common perception that influence is relevant to determine the global behavior of the society and thus it can be used to enforce cooperation by targeting an adequate initial set of individuals or to analyze global choice processes. Here we propose centrality measures that can be used to analyze the relevance of the actors in process related to spread of influence. In (39) it was considered a multiagent system in which the agents are eager to perform a collective task depending on the perception of the willingness to perform the task of other individuals. The setting is modeled using a notion of simple games called influence games. Those games are defined on graphs were the nodes are labeled by their influence threshold and the spread of influence between its nodes is used to determine whether a coalition is winning or not. Influence games provide tools to measure the importance of the actors of a social network by means of classic power indices and provide a framework to consider new centrality criteria. In this paper we consider two of the most classical power indices, i.e., Banzhaf and Shapley-Shubik indices, as centrality measures for social networks in influence games. Although there is some work related to specific scenarios of game-theoretic networks, here we use such indices as centrality measures in any social network where the spread of influence phenomenon can be applied. Further, we define new centrality measures such as satisfaction and effort that, as far as we know, have not been considered so far. Besides the definition we perform a comparison of the proposed measures with other three classic centrality measures, degree, closeness and betweenness. To perform the comparison we consider three social networks. We show that in some cases our measurements provide centrality hierarchies similar to those of other measures, while in other cases provide different hierarchies.