Centrality measure is an important concept in networks. It indicates the relative importance of nodes in a network. Various centrality measures have been proposed in the literature, such as degree centrality, closeness centrality etc. Practically all these measures are some values based on the properties of the node concerned. Eigenvector centrality takes into account the centrality value of th...

This study proposes a different angle to social network analysis that evaluates patent value and explores its influencing factors using the network centrality and network position. This study utilizes a logistic regression model to explore the relationships in the LED industry between patent value and network centrality as measured from out-degree centrality, in-degree centrality, in-closeness ...

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...

Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to a large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connecte...

We present a new fully dynamic algorithm for maintaining betweenness centrality (BC) of vertices in a directed graph G = (V,E) with positive edge weights. BC is a widely used parameter in the analysis of large complex networks. We achieve an amortized O(ν · log n) time per update, where n = |V | and ν bounds the number of distinct edges that lie on shortest paths through any single vertex. This...

The growth of Big Data has seen the increasing prevalence of interconnected graph datasets that reflect the variety and complexity of emerging data sources. Recent distributed graph processing platforms offer vertex-centric and subgraphcentric abstractions to compose and execute graph analytics on commodity clusters and Clouds. Näıve translation of existing graph algorithms to these programming...

More than fifteen years after the beginning of the development of AutoGraphiX (AGX), a third version of the software is made available. Since the program was rewritten from scratch, it was the opportunity to look forward and consider new avenues. From the user’s point of view, the interface is completely changed, which allows the display of multiple information which was not possible in the pre...

The problem of community detection receives great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimate of this model is equivalent to label propagation wi...