Network theory

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

Evaluation of Highway Network Node Importance via Node Benefit Function and Weighted Node Betweenness

Abstract: In a highway network, nodes playing a significant role could have an extreme effect on operation efficiency and other performance measures, so evaluating these important nodes is thought to be a critical process in highway network management and operation. This paper proposes a new method for evaluating the importance of nodes in highway networks, based on the Graph Theory and Network Theory. First, a benefit function is proposed for each node with weights of GDP, mileage, capacity, and so forth, and a benefit indicator is calculated, which represents the local importance of each node. Second, weighted node betweenness was estimated for describing the global importance throughout the whole network. After that, the overall importance for each node was calculated by weighted average of both local and global importance. Finally, a verification of the new method was conducted with a highway network among five eastern provinces and cities in China. The results indicate that the overall importance estimated by this method matches with reality.

Connecting the dots to disconnect them: a study into network evolution and dynamics for analyzing terrorist networks

Abstract: For a long time, a lack of sucient data has been an obstacle to the intelligence community. This study providesan integrated approach which combines network theory and data mining to analyze 1440 instances of terrorismthat occurred up to 2002. The study reveals interesting patterns on the evolution of these terrorist organizationsover two decades.Keywords: Data mining, Network dynamics, Network evolution, Network mining 1. INTRODUCTION Data mining 1 is the process of discovering novel patterns in databases. Traditional data mining techniques -such as machine learning - assume that the attributes are independent and that the values that each of theseattributes can take are also independent. This assumption made theoretical analysis of data mining techniquesfeasible, but unfortunately it is an unrealistic assumption in many real life situations. The second consequenceof this assumption is that most techniques require a considerable amount of data to reach any useful conclusions.Relaxing the inaccurate assumption that the attributes are independent and the values that each attributecan take are also independent would naturally mean that there are virtual links connecting these attributes andtheir values. As such, one can visualize the relationship between these attributes and their values as a network.This is a much richer representation that can be used to substantiate data analysis with some of the rigorousmeasures used in social network theory. This approach we call network mining.In network mining, a number of variations exist in the literature. One is known as link analysis (see forexample

A Network of Network Theories

Abstract: This chapter examines the ideas that currently inform our understanding of networks and how they affect networked learning. The chapter brings together a number of different theoretical approaches to networks from diverse intellectual traditions, and it explores what kinds of insights they can offer in developing a theory of networked learning. The chapter begins by discussing what a network might be. Networks are spoken about as if there was a single or simple definition which is common to all network theories. Only a little investigation reveals this is not the case, so the chapter begins with a brief outline of the way networks are described and discussed in a general way, prior to embarking on a more detailed discussion of three sources of network theory. The three sources drawn on in this chapter are: (a) scientific and mathematical theories and descriptions; (b) social networks and social network analysis (SNA); (c) actor-network theory (ANT).

Random Simulations of Braess's Paradox

Abstract: This paper uses network theory to simulate Nash equilibria for selfish travel within a traffic network. Specifically, it examines the phenomenon of Braess’s Paradox, the counterintuitive occurrence in which adding capacity to a traffic network increases the social costs paid by travelers in a new Nash equilibrium. It also employs the measure of the price of anarchy, a ratio between the social cost of the Nash equilibrium flow through a network and the socially optimal cost of travel. These concepts are the basis of the theory behind undesirable selfish routing to identify problematic links and roads in existing metropolitan traffic networks [8], suggesting applicative potential behind the theoretical questions this paper attempts to answer. New topologies of networks which generate Braess’s Paradox are found. In addition, the relationship between the number of nodes in a network and the number of occurrences of Braess’s Paradox, and the relationship between the number of nodes in a network and a network’s price of anarchy distribution are studied. RANDOM SIMULATIONS OF BRAESS’S PARADOX 3