Network theory

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

Massive parallelism with gpus for centrality ranking in complex networks

Abstract: Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex networks, which can be considered equivalent to undirected graphs, provides an useful metric of the relative importance of each node inside the evaluated network. The knowledge on which the most central nodes are, has various applications, such as improving information spreading in diffusion networks. In this case, most central nodes can be considered to have higher influence rates over other nodes in the network. The main purpose in this work is developing a GPU based and massively parallel application so as to evaluate the node centrality in complex networks using the Nvidia CUDA programming model. The main contribution of this work is the strategies for the development of an algorithm to evaluate the node centrality in complex networks using Nvidia CUDA parallel programming model. We show that the

Interests and Knowledge

Abstract: The careful reader will have noted our use of the term ‘interest’ at a number of places in earlier chapters and will rightly have concluded that this concept plays an important role in the network theory of knowledge. The purpose of the present chapter is to elaborate upon this notion. We may start by recalling that we talked, earlier, of the ‘workability’ or ‘utility’ of networks and argued that this is the acid test of the success thereof. If a network allows the individual to interact satisfactorily with his or her environment, then it is upheld and reinforced. If, on the other hand, the behaviour of the environment is unpredictable from the standpoint of the network (as in the case of Xaanthi and his notion of ‘mammal’ or the child and the parrot) then this network is undermined and susceptible to change. However, in order to judge network workability, the individual has to ask questions of it. The network is not an idle set of terms disconnected from reality. It is a tool that may be used when the individual is trying to achieve a goal or solve a problem. It is an instrument, fashioned for certain purposes. It is a resource that allows the individual to move from A to B in his or her environment. It is, as we saw, like a map that will serve certain interests and not others.

The Building of E-Commerce Transaction Network Based on Multi-agent and CAS Theory

Abstract: In this paper, analyzed the characteristic of E-Commerce Transaction Network --indetermination, non- linearity, complexity, variety, discreteness, dynamic from the point of view of system, suggested that the modeling and simulation for E-Commerce Transaction Network would simulation for queuing, inventory and network. Complex adaptive system (CAS) theory is used to analyze the e-commerce system and the concept of e-commerce transaction network is proposed. The concepts of degree centrality and between’s centrality in social network analysis are used to analyze the derived 1-mode networks, and the positions of the buyers, sellers and products in the transaction network can be determined visually.

Mining User-Generated Path Traversal Patterns in an Information Network

Abstract: This paper studies patterns occurring in user-generated click paths within the online encyclopedia Wikipedia. The click path data originates from over seven million goal-oriented clicks gathered from the Wiki Game, an online game in which the goal is to find a path between two given random Wikipedia articles. First we propose to use node-based path traversal patterns to derive a new measure of node centrality, arguing that a node is central if it proves useful in navigating through the network. A comparison with centrality measures from literature is provided, showing that users generally "know" only a relatively small portion of the network, which they employ frequently in finding their goal, and that this set of nodes differs significantly from the set of central nodes according to various centrality measures. Next, using the notion of sub graph centrality, we show that users are able to identify a small yet efficient portion of the graph that is useful for successfully completing their navigation goals.

Chapter 6 Probabilistic networks and network algorithms

Abstract: Publisher Summary This chapter discusses probabilistic networks and network algorithms. Probability enters into the theory of networks and network algorithms in several different ways. The most direct way is through probabilistic modeling of some aspect of the network. For example, in some freight management models the cost of transportation along the arcs of the network are modeled by random variables. In models such as these probability helps us grasp a little better a world that comes with its own physical randomness. A second important way probability enters is through more stylized stochastic models where the aim is to provide deeper insight into our technical understanding of the methods of operations research. Here there is considerably less emphasis on building detailed models that hope to capture aspects of randomness that live in a specific application context; rather, the aim is to provide mathematically tractable models of reasonable generality that can be used to explore a variety of different computational or estimation methods. Among the types of issues that have been studied in such models are the efficacies of deterministic algorithms and of deterministic heuristic methods. Many of the 'average case' analyses of algorithms would fit into this second role for probability. The third path by which probability enters into network theory is through randomized algorithms. This is the newest of the roles for probability, but it is a role that is of increasing importance. To make certain of the distinction that makes an algorithm 'randomized,' consider a version of depth-first search where one chooses the next vertex to be explored by selecting it at random from a set of candidates.