Eigenvectors, and the related centrality measure Bonacich’s c(β), have advantages over graph-theoretic measures like degree, betweenness, and closeness centrality: they can be used in signed and valued graphs and the beta parameter in c(β) permits the calculation of power measures for a wider variety of types of exchange. Degree, betweenness, and closeness centralities are defined only for clas...

In this study we seek to understand the factors differentiating successful from unsuccessful software projects. This article develops and tests a model measuring the impact on software project performance of (1) software products’ design architectures and (2) developers’ positions within collaborative networks. Two indicators of project success are used: product quality and project velocity. Tw...

This paper focuses on Open Source (OS) social networks. The literature indicates that OS networks have a few nodes with a number of relationships significantly higher than the network’s average, called hubs. It also provides numerous metrics that help verify whether a node is a hub, called centrality metrics. This paper posits that higher values of centrality metrics are positively correlated w...

Research on network analysis, which is used to analyze large-scale and complex networks such as social networks, protein networks, and brain function networks, has been actively pursued. Typically, the networks used for network analyses will contain multiple errors because it is not easy to accurately and completely identify the nodes to be analyzed and the appropriate relationships among them....

We establish a relationship between decay centrality and two widely used and computationally cheaper measures of centrality, namely degree and closeness. We show that for low values of the decay parameter the nodes with maximum decay centrality also have maximum degree, whereas for high values of the decay parameter they also maximize closeness. For intermediate values, we provide sufficient co...

Processing large graphs is an emerging and increasingly important computation in a variety of application domains, from social networking to genomics and marketing. One of the important and computationally challenging structural graph metrics is node betweenness centrality, a measure of influence of a node in the graph. The best known algorithm for computing exact betweenness centrality runs in...

Collaborative learning has become a dominant learning apparatus for higher level learning objectives. Much of the psychological and social mechanisms operating under this complex group activity, however, is not yet well understood. The purpose of this study was to investigate the effects of college students' communication competence and degree centralities of their social networks on learning o...

The betweenness metric has always been intriguing and used in many analyses. Yet, it is one of the most computationally expensive kernels in graph mining. For that reason, making betweenness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which compresses a network and shatters it into pieces so that the centrality computa...

Several centrality measures were introduced to identify essential components and compute components’ importance in networks. Majority of these centrality measures are dominated by components’ degree due to their nature of looking at networks’ topology. We propose a novel essential component identification model, bridging centrality, based on information flow and topological locality in scale-fr...

Centrality metrics have proven to be of a major interest when analyzing the structure of networks. Given modern-day network sizes, fast algorithms for estimating these metrics are needed. This paper proposes a computation framework (named Filter-Compute-Extract) that returns an estimate of the top-k most important nodes in a given network. We show that considerable savings in computation time c...