Network theory

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

Power Factor Typology Through Organizational and Network Analysis: Using environmental policy networks as an illustration

Abstract: This work is based on a doctoral research. Our main question is: who can be powerful and when. We assume that power is a function of network and organizational (actor-related) characteristics and thus not every actor (organisation) can be powerful in every network. Power and institutional theories will be operationalized, completed and specified by the results. Five types of power factors will be proposed, which are combinations of organizational and network characteristics that combine to produce high power synergy and low inconsistency. The first dimension of power is trust: the trustee leads the one who trusts. The second dimension is financial incentive: the gift giver influences the gift receiver. The third dimension is irreplaceability. This is an operationalization of general system theory which operationalizes the exchange power model. Although the dependent variable (power) will be calculated by the systemic approach, the independent variables will be culled from New Institutionalism. For this purpose, a combination of the Theory of Organized Interests and Network Theory is necessary. These theories will be specified throughout our results. The typology of power factors (organizational and network characteristics) was derived from both inductive and deductive processes. The organizational factors have been deduced from certain theories: the "lawful" type from contingency theory and mobilization of bias, the "trustworthy" from the resource dependence model, the "little brother" from the transaction-cost and resource dependence model, the "omniscient" type from decision- making theory, and the "re-distributor" type from decision-making theory and hypotheses on the role of monitoring information. Afterwards, the deduced organizational factors of each type have functioned as a basis for the induction of network factors, which proved to reach highest power synergy with the organizational factors through stepwise regression.Our methodology is a statistics-based vector algebra. We measured 108 indicators in 234 cases from 12 environmental policy networks in 8 European countries (Denmark, Finland, Germany, Greece, Ireland, Spain, Sweden, UK). In general, "trust" makes up 82% of the power composition, while "financial incentive" is only 8% and "irreplaceability" only 10%. Not all the network characteristics and organized interest models proposed until now have proven relevant to power, rather only some of them in certain combinations. We classified these combinations into five types: The "lawful" type: An actor with a multidisciplinary team that is lawful but not state-controlled has optimal chances in "non crowded" and mono-sectoral networks with intensive state contacts, where the state does not play any important role. The "trustworthy" type: A trustworthy actor with a multidisciplinary team has optimal chances in a "non-crowded" network with intensive state contacts and low importance of state. The "little brother" type: An actor who has powerful partners and various financing resources has optimal chances in a monosectoral network with "equal chances", where many possible contacts remain unexplored. The "omniscient" type: A powerful actor who implements its power by imposing general or scientific information as "important" on a network with little material needs. The "redistributor" type: A powerful actor who receives occasional general information and reconstructs it in order to provide "important" general and scientific information. It has optimal chances in a network with no scientific links.The equilibrium between the advantages and disadvantages of the method of complete network analysis has motivated thoughts about future research questions regarding the quality of regression and the insights of Heckman on the weakness of self-selection. A combined strategy of qualitative and quantitative research is necessary in order to make policy consulting applicable to politics and further theorizing more accurate.

Impact of hierarchical modular structure on ranking of individual nodes in directed networks

Abstract: Many systems, ranging from biological and engineering systems to social systems, can be modeled as directed networks, with links representing directed interaction between two nodes. To assess the importance of a node in a directed network, various centrality measures based on different criteria have been proposed. However, calculating the centrality of a node is often difficult because of the overwhelming size of the network or because the information held about the network is incomplete. Thus, developing an approximation method for estimating centrality measures is needed. In this study, we focus on modular networks; many real-world networks are composed of modules, where connection is dense within a module and sparse across different modules. We show that ranking-type centrality measures, including the PageRank, can be efficiently estimated once the modular structure of a network is extracted. We develop an analytical method to evaluate the centrality of nodes by combining the local property (i.e. indegree and outdegree of nodes) and the global property (i.e. centrality of modules). The proposed method is corroborated by real data. Our results provide a linkage between the ranking-type centrality values of modules and those of individual nodes. They also reveal the hierarchical structure of

A Faster Algorithm to Update Betweenness Centrality After Node Alteration

Abstract: Betweenness centrality is a centrality measure that is widely used, with applications across several disciplines. It is a measure which quantifies the importance of a vertex based on its occurrence in shortest paths between all possible pairs of vertices in a graph. This is a global measure, and in order to find the betweenness centrality of a node, one is supposed to have complete information about the graph. Most of the algorithms that are used to find betwenness centrality assume the constancy of the graph and are not efficient for dynamic networks. We propose a technique to update betweenness centrality of a graph when nodes are added or deleted. Our algorithm experimentally speeds up the calculation of betweenness centrality (after updation) from 7 to 412 times, for real graphs, in comparison to the currently best known technique to find betweenness centrality.