There is an ever-increasing interest in investigating dynamics in timevarying graphs (TVGs). Nevertheless, so far, the notion of centrality in TVG scenarios usually refers to metrics that assess the relative importance of nodes along the temporal evolution of the dynamic complex network. For some TVG scenarios, however, more important than identifying the central nodes under a given node centra...

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality ...

Geographical centrality is an evolving concept that differs from one perspective to another at different stages. The unprecedented development of high-speed information and transportation networks has highlighted the important role of space of flows and has restructured the mode of spatial interaction. The geographical centrality analysis method based on relational networks currently becomes th...

Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centrality-based network management. The problem has many applications such as verifying the robustness of the networks and controlling or improving the entity dis...

This paper discusses the conceptualization, measurement, and interpretation of centrality in affiliation networks. Although centrality is a well-studied topic in social network analysis, and is one of the most widely used properties for studying affiliation networks, virtually all discussions of centrality and centralization have concerned themselves with one-mode networks. Bonacich's work on s...

The notion of return probability – explored most famously by George Pólya on d-dimensional lattices – has potential as a measure for the analysis of networks. We present an efficient method for finding return probability distributions for connected undirected graphs. We argue that return probability has the same discriminatory power as existing k-step measures – in particular, beta centrality (...

Assortativity index (A. Index) of real-world network graphs has been traditionally computed based on the degree centrality metric and the networks were classified as assortative, dissortative or neutral if the A. Index values are respectively greater than 0, less than 0 or closer to 0. In this paper, we evaluate the A. Index of real-world network graphs based on some of the commonly used centra...