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...

Wittrock, Zachary, R. M.A., Purdue University, August 2014. Dismantling the Great Wall of Prejudice: A Look at Centrality and Issue Importance in the Perpetuation of Influence and Prejudice Reduction. Major Professor: Torsten Reimer This study sought to utilize social network analysis to better understand how prejudice reduction can be spread from one person to another via interpersonal influen...

In this paper, we propose a method for rapid computation of group betweenness centrality whose running time (after preprocessing) does not depend on network size. The calculation of group betweenness centrality is computationally demanding and, therefore, it is not suitable for applications that compute the centrality of many groups in order to identify new properties. Our method is based on th...

A manufacturing industry contributes around 10% Malaysian economy. It provides economic opportunities for related industries and business. However, the number of accidents in manufacturing sector, including fatal accidents, has been increased from time to time. To understand the real situation, in this paper, we used a correlation network analysis to analyze 43 characteristics of worker’s behav...

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...

We introduce a novel centrality metric, the neighbor vector centrality. It is a measurement of node importance with respect to the degree distribution of the node neighbors. This centrality is explored in the context of several networks. We use attack vulnerability simulation to compared our approach with three standard centrality approaches. While for real-world network our method outperforms ...

We investigate how a selection of centrality measures can be used to differentiate roles of characters in Jean-Jacques Rousseau’s autobiography Les Confessions. We define methods to build automatically a network of characters, based on their co-occurrences. In the resulting network, each character of the novel is a node connected to other nodes representing other characters. We rank these with ...

This study involves a network analysis on Twitter discourse about gun control. In a test of competing models explaining social network dynamics, preferential attachment versus reciprocity, the latter received stronger support for the data set as a whole. Outdegree centrality at Time 1 was more closely correlated with degree centrality at Time 2 than was indegree centrality at Time 1. However, a...