Network theory

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

Theoretical basis of supply management: the netwerk theory in supply management

Abstract: During the last decades several so called ‘grand theories’ have been proposed to underpin supply management and purchasing. They are meant to progress these disciplines by providing a theoretical basis to the field. Next to theories as for instance the resource based view, the resource dependency theory, the transaction cost economics or the agency theory, the network theory has been put on the agenda of researchers. The network theory describes the relationships between companies located in the same supply chain. The concept developed over time from the simple consideration of relationships or strategic alliances between just two companies, towards the explanation of relationships between several counterparts within a supply network, are they suppliers, organisations, buyers, customers or manufacturers. However, no clear consensus have emerged in the field of the contribution of the theory towards supply management and purchasing yet. Therefore, this literature review aims to provide insight in the applicability of the network theory for supply management and purchasing. Besides describing general facts about the theory, namely the history, the underlying assumptions, the description of an empirical test retrieved from the literature, a core concept of the theory is developed in order to provide insight in the most important hypotheses and variables of the theory. These factors helped to reveal the contribution of the theory for purchasing and supply management. For this, a 3-phase model including four major decision points for purchasers was developed to show the applicability of the theory for each aspect. Lastly, a matrix was developed in order to create an overview of the most important contributions which can be used by managers and purchasers interested in the field of networks in supply management

A penalty box approach for approximation betweenness and closeness centrality algorithms

Abstract: Centrality metrics are used to find important nodes in social networks. In the days of ever-increasing social network sizes, it becomes more and more difficult to compute centrality scores of all nodes quickly. One of the ways to tackle this problem is to use approximation centrality algorithms using sampling techniques. Also, in situations where finding only high value individuals/important nodes is the primary objective, accuracy of rank ordering of nodes is especially important. We propose a new sampling method similar to Tabu search, called “penalty box approach,” which can be used for approximation closeness and betweenness algorithms. On a variety of graphs we experimentally demonstrate that this new method when combined with previously known methods, such as random sampling and linear scaling, produces better results. The evaluation is done based on two measures that assess quality of rank ordered lists of nodes when compared against the true lists based on their closeness and betweenness scores. Effects of graph characteristics on the parameters of the proposed method are also analyzed.

A Novel Method of Centrality in Terrorist Network

Abstract: The problem of assigning centrality values to nodes in a graph has been widely investigated during recent years. It is especially important to measure nodes' centrality in terrorist and transportation networks. Because these nodes could be anything important, could spread, accept and destroy some essential information, we should try our best to determine the key node in a network to reduce the damage and jam. The social network analysis (SNA) is used to measure the key node in a network. A terrorist network can be modeled as a generalized network (graph) consisting of nodes and links. Many methods from social network analysis and graph theory can be used to determine central nodes in a network, which are helpful for network destabilization purposes. This paper provides a centrality measure for nodes in terrorist network, which takes consideration of nodes' information from others and themselves, and Three Degree of Influence Rule. The method called Influence Index is used for known terrorist network, i.e. 9.11 hijacking. Through comparison, we get better results than some classical centrality methods, for example Degree, Between ness, Eigenvector and Laplacian Energy etc.

Modeling and analysis of massive social networks

Abstract: Traditional epidemiological research has focused on rate-based differential-equation models with completely mixing populations [6, 7, 44]. Although successful in explaining certain phenomena of disease spreading, the traditional approach is unable to deal with disease spreading in realistic massive social networks, where most people only mix locally with few other people. We have develop an approach based on a combination of network theory and discrete-event simulations to study epidemics in large urban areas ; which do not assume complete mixing populations. Our results include (1) detailed structural and temporal analyses of the social contact networks produced by TRANSIMS [10], a simulator for detailed transportation/traffic studies; (2) realistic simulation of contagious diseases (e.g., smallpox) on the social contact networks through EpiSims [32], a simulation-based analytical tool to study the spread of infectious diseases in an urban environment; (3) identifying a number of new measures that are significant for understanding epidemics and for developing new strategies in policy planning; (4) introduction of random graph models for theoretical analysis of the structural and algorithmic aspects of the social networks; and (5) combinatorial formulations and approximation algorithms for performing quarantine, vaccination and sensor placement, as aids to decision-making. The social network that we have mostly dealt with is for the city of Portland; Oregon, USA, developed as a part of the TRANSIMS/EpiSims project at the Los Alamos National Laboratory. The most expressive social contact network is a bipartite graph representing people and locations; edges represent people visiting locations on a typical day. We also build random graph models to generate a family of social networks by taking as input some basic parameters of the Portland social network, and analyze social networks generated by these models.

A study of the Immune Epitope Database for some fungi species using network topological indices.

Abstract: In the last years, the encryption of system structure information with different network topological indices has been a very active field of research In the present study, we assembled for the first time a complex network using data obtained from the Immune Epitope Database for fungi species, and we then considered the general topology, the node degree distribution, and the local structure of this network We also calculated eight node centrality measures for the observed network and compared it with three theoretical models In view of the results obtained, we may expect that the present approach can become a valuable tool to explore the complexity of this database, as well as for the storage, manipulation, comparison, and retrieval of information contained therein