Network theory

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

Evaluating action learning: a critical realist complex network theory approach

Abstract: This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around complex systems and networks Network theory is the general proposition that the world can be understood as a system of nodes or links at recursive levels (individuals, groups/departments, organisations, clusters and industries etc) and includes, but is by no means limited to, social networking The paper will argue that action learning can help organisations and groups, understood as networks, balance the destabilising tendencies to explosion and implosion, and, rightly used, can help prevent network distortion (over-dominance of one group of stakeholders)

Multiparameter sensitivity problems in network theory

Abstract: The paper presents a survey of the literature as it pertains to the problem of multiparameter sensitivity in network theory. In particular, a critical appraisal is made of several multiparameter sensitivity functions, and the problems involved in their computation are considered. Several procedures for their evaluation are described, with attention focused on the auxilliary network approach of Leeds and Ugron and the adjoint network approach of Director and Rohrer. Another approach considered is the so-called direct approach, in which an algorith, is used to obtain directly the partial derivaties of the network function.

Stability of Consensus Node Orderings Under Imperfect Network Data

Abstract: In complex network analysis, the problem of ranking individual nodes based on their importance has attracted increasing attention from the scientific community due to its vast application, such as identification of influential spreaders for viral marketing or epidemic control, bottlenecks for traffic congestion control, and so on. The growing literature proposes a number of measures to determine the rank order of the network entities where complete information about the nodes and their interaction is available. Degree centrality, PageRank, eigenvector centrality, closeness centrality are few such popular measures. In most real-life scenarios, however, the information about the underlying network is incomplete or affected due to noise. The few works that study the effects of incomplete information on the rank orders show the vulnerability of the rank orders in various topologies. In this paper, we investigate the effects of noise, both random and nonrandom, on the aggregated rank orders determined from the degree, PageRank, eigenvector centrality, and closeness centrality-based rankings. This paper reveals an important insight that even the simple Borda Count ranking has the potential to improve on the accuracy of rank orders in networks with uncertainty. This paper shows the existence of stable nodes in various networks and indicates that the design of the consensus approach based on the properties of the stable nodes can further improve the stability of the rank orders.

Analysis of a social network

Abstract: Paper presents an analysis of a social network using a graph, and also taking into account the 802 post that are created by 114 users representing a social network interaction among the users. Input parameters are represented by the adjacency matrix, which is a kind of relationship between users who are nodes of social networks. Data analysis used the software UCINET 6, which is the adjacency matrix input parameter. Obtained results, as well as their interpretation, are related to the following measures: centrality (degree, betweeness, closeness) clustering coefficient, density, reach, geodesic distance, eigenvector.

The Node Influence Analysis in Social Networks Based on Structural Holes and Degree Centrality

Abstract: The analysis of node influence plays important role in product marketing, public opinion analysis, disease transmission and other fields. Researchers have proposed a variety of methods to measure node influence, with the rapid expansion of the scale of social networks, Degree Centrality algorithm attracts much attention for its lowest time complexity, however, its result is not sufficiently accurate because it considers only the local node information and not reflects the impact of topological connections among neighbor nodes. To solve this problem, we proposed a novel measure based on Structural Holes and Degree Centrality(SHDC). Our measure combines Structural Hole and Degree Centrality to measure the node influence. It uses Degree Centrality to make a fast and coarse distinction between the influence of nodes and uses Structure Hole to reflect the impact of topological connections among neighbor nodes, which improves the ability to distinguish the influence of nodes in the low time complexity. Experimental results show that the SHDC algorithm can more accurately measure the influence of nodes than Degree Centrality and Structural Hole and it has stronger applicability.