Network theory

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

Computation of reverse degree-based topological indices of hex-derived networks

Abstract: Network theory gives an approach to show huge and complex frameworks through a complete arrangement of logical devices. A network is made is made of vertices and edges, where the degree of a vertex refers to the number of joined edges. The degree appropriation of a network represents the likelihood of every vertex having a particular degree and shows significant worldwide network properties. Network theory has applications in many disciplines like basic sciences, computer science, engineering, medical, business, public health and sociology. There are some important networks like logistical networks, gene regulatory networks, metabolic networks, social networks, derived networks. Topological index is a numerical number assigned to the molecular structure/netwrok which is used for correlation analysis in physical, theoretical and environmental chemistry. The hex-derived networks are created by hexagonal networks of dimension $ t $, these networks have an assortment of valuable applications in computer science, medical science and engineering. In this paper we discuss the reverse degree-based topological for third type of hex-derived networks.

Simulation of network-enabled electronic warfare metrics to assess the value of networking in a general information and radar topology

Abstract: : This thesis explores information network metrics, the concept of netted radar, and network theory in a network-centric warfare environment. It begins with a discussion of the relationship between the network space and the battlespace. MATLAB simulations are developed to demonstrate the concepts and quantify the network metrics discussed for important information and netted radar configurations. The effect of electronic attack is also addressed. Simulation results to demonstrate the signal-to-noise ratio performance with and without network synchronization are shown, including the degradation due to electronic attack.

A Genetic Algorithm for Modularity Density Optimization in Community Detection - TI Journals

Abstract: Many complex systems can be modeled as complex networks, so we can use network theory to study this models. One important feature in networks is community structure, i.e. the organization of nodes in communities, with many edges joining nodes of the same community and comparatively few edges joining nodes of different communities. A large number of community detection algorithms have been proposed in the last decade. Many of these algorithms use modularity as function to optimize. The modularity has been exposed that have resolution limits and contains an intrinsic scale that depends on the total size of edges in the network. Modules smaller than this scale may not be detected even in the extreme case that they are complete graphs connected by single bridges. Recently a new quantitative measure has been proposed for evaluating the partition of a network into communities called modularity density. In this paper we propose a genetic algorithm to optimize this quantitative measure. We use the matrix representation that make it easier to mutate and crossover the individuals. Adjusted Rand Index (ARI) is used for measuring performance of algorithm. The experimental tests using artificial, LFR benchmark and real world networks with known community structure, revealed the effectiveness of the algorithm.