Network theory

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

Centrality-based routing for Wireless Sensor Networks

Abstract: The use of topological features, more specifically, the importance of an element related to its structural position is a subject widely studied. For instance, complex networks theory provides some general use of centrality measures that have been applied to a large variety of knowledge fields. This work aims to use centrality information in the design of routing algorithms for Wireless Sensor Networks (WSN). In this context, we propose a new topological measure, the Sink Betweenness, a distributed algorithm to calculate it, and devise a tree-based routing algorithm that takes advantage of it. Simulations comparing our approach to some well-known algorithms show that our tree-based algorithm improves the routing overlap, favoring data fusion.

Network Analysis and Visualizations of Water Resources Infrastructure in California: Linking Connectivity and Resilience

Abstract: Connectivity is important to the structure and function of systems. In water infrastructure systems, connections between components determine flow patterns and operational flexibility. Network theory can provide tools to assess and view connectivity in water resource networks that augment current methods in water resource systems analysis. This paper presents an analysis of connectivity and resilience in the network of California’s water resources infrastructure using network theory visualization and metrics. Applying these techniques to a link-node network used in a statewide hydroeconomic model of water management in California, the analysis shows how such metrics are useful to describe aspects of connectivity and identify important system components. Both the entire system and the San Francisco Bay Area subsystem are analyzed using measures of centrality, spacing, and linkage. The analysis also assesses the effects of network degradation by removing selected components, revealing complex relati...

A shapley value-based approach to determine gatekeepers in social networks with applications

Abstract: Inspired by emerging applications of social networks, we introduce in this paper a new centrality measure termed gatekeeper centrality. The new centrality is based on the well-known game-theoretic concept of Shapley value and, as we demonstrate, possesses unique qualities compared to the existing metrics. Furthermore, we present a dedicated approximate algorithm, based on the Monte Carlo sampling method, to compute the gatekeeper centrality. We also consider two well known applications in social network analysis, namely community detection and limiting the spread of mis-information; and show the merit of using the proposed framework to solve these two problems in comparison with the respective benchmark algorithms.