Social Network Centrality Measures Research Articles

Hiding From Centrality Measures: A Stackelberg Game Perspective

Centrality measures can rank nodes in a social network according to their importance. However, in many cases, a node may want to avoid being highly ranked by such measures, e.g., as is the case with terrorist networks. In this work, we study a confrontation between the seeker—the party analyzing a social network using centrality measures—and the evader—a node attempting to decrease its ranking according to such measures. We analyze the possible outcomes of modifying, i.e., adding or removing, a single edge by the evader, showing that even without complete knowledge about the network, the effects of the modification on the evader's ranking can often be predicted. We study the computational complexity of finding a set of modifications that reduce the evader's centrality ranking in an optimal way, proving that these decision problems are NP-complete. Moreover, we provide a 2-approximation for the degree centrality, and logarithmic approximation boundaries for the closeness and betweenness centralities. Finally, we define and investigate a Stackelberg game between the seeker and the evader, providing a Mixed Integer Linear Programming formulation of finding an equilibrium. Altogether, we provide a thorough analysis of the strategic aspects of hiding from centrality measures in social networks.

Urbanity as a Process and the Role of Relative Network Properties—A Case Study From the Early Iron Age

A manifold of concepts of urbanity have been discussed in the past and many criteria of towns have been developed. These criteria, e. g. size, population,legal aspects, way of life, structural and functional approaches are insufficient, because they cover just a part of the phenomenon and because they partly use fixed and arbitrary thresholds. We turn to an understanding of urbanity as a process which fills and shapes the scenery of the buildings and people and which involves complexity as one of the main drivers. In this sense we understand urbanity as a process of adaption to changed requirements or contexts in a complex settlement system, triggered by size, attracted by exemplary solutions and characterized by the emergence of new structures. With this paper we address the issues of relativity in the urbanity process. As adaption process, urbanity is relative in the sense, that other places may have gained better or worse adaption. Concerning networks, this means that network centrality measures have to be adapted to geographical networks. This paper applies this concept to the Princely Seats of the Early Iron Age with a special focus on the Heuneburg. A very limited part of the material culture is used to represent similarities and interaction between the different nodes. For this purpose we use fibulae which allow rather good dating and hence ensure a narrow time slice for the network analysis. Fibulae also provide a limitation to a certain social segment, which can be addressed as a middle class. This paper is intended to contribute to the problem of addressing the rather complex issue of urbanity using rather simple approaches such as network analysis. We pay attention to a tight integration of theory and method as well as to certain conceptual issues. This paper has two main results. First, we develop a consistent approach to apply social network centrality measures on geographical networks. Secondly, the role of a kind of middle class in the course of urbanity processes is enlightened.

Diffusion of cryptocurrencies: web traffic and social network attributes as indicators of cryptocurrency performance

This study investigates the popularity, global and local presence of the top cryptocurrency websites in terms of market capitalization, and geographic distribution of cryptocurrency information on the web by mapping the information network of cryptocurrency sites. The study also explores whether web traffic and social network metrics serve as valuable indicator of financial performance of cryptocurrencies. Both citation and social network analyses were employed. The results suggest that bitcoin.org, bitcoin.com and steemit.com are the most popular sites based on the number of hit counts. Information production and consumption about coins are concentrated in European and Asia–Pacific regions. Germany and technology startup companies are the most central sources for cryptocurrency-related information in the network. This study also found positive cross correlation outcomes between web traffic, social network attributes, and cryptocurrency performance indicators. The finding demonstrates that the number of TLDs and multiple social network centrality measures are useful indicators of cryptocurrency market capitalization, trading volume, and price.

Structural features and evolutionary mechanisms of industrial symbiosis networks: Comparable analyses of two different cases

Recently, industrial symbiosis networks (ISNs) have become popular development patterns for regional industrial systems in many countries. As such, it is important to analyze the underlying structural characteristics and evolutionary mechanisms of ISNs in diverse situations. This paper's contributions to the literature are threefold. (1) Based on all types of centrality and centralization measures in social networks (the direction of links included), the structural similarities, differences, and associated mechanisms of two typical and comparable ISNs—the spontaneously formed Kalundborg ISN in Denmark and the planned Lubei ISN in China are the first to be revealed. (2) Based on average clustering coefficient, average path length, and power law distribution of degree from complex network theory, the small-world and scale-free effects of the two ISNs are compared. (3) Network fragmentation is then defined to compare the two ISNs under the selective attack of removing critical enterprises from them at different development stages. As such, this paper not only extends the methodology of analyzing the structural characteristics of different ISNs, but also provides guidance for managers on the construction of actual eco-industrial parks.

Study on centrality measures in social networks: a survey

Social networks are absolutely a useful and important place for connecting people within the world. A basic issue in a social network is to identify the key persons within it. This is why different centrality measures have been found over the years. In this survey paper, we present past and present research works on measures of centrality in social network. For this plan, we discuss mathematical definitions and different developed centrality measures. We also present some applications of centrality measures in biology, research, security, traffic, transportation, drug, class room. At last, our future research work on centrality measure is given.

Algorithms for fast estimation of social network centrality measures

Centrality measures are extremely important in the analysis of social networks, with applications such as the identification of the most influential individuals for effective target marketing. Eigenvector centrality and PageRank are among the most useful centrality measures, but computing these measures can be prohibitively expensive for large social networks. This paper explores multiple approaches to improve the computational effort required to compute relative centrality measures. First, we show that small neural networks can be effective in fast estimation of the relative ordering of vertices in a social network based on these centrality measures. Then, we show how network sampling can be used to reduce the running times for calculating the ordering of vertices; degree centrality-based sampling reduces the running time of the key node identification problem. Finally, we propose the approach of incremental updating of centrality measures in dynamic networks.

Algorithms for fast estimation of social network centrality measures

Algorithms for fast estimation of social network centrality measures

Social capital from online discussion forums: Differences between online and blended modes of delivery

<p class="AJET-Abstract">This study explored the concept of social capital in higher education contexts by investigating student discussion forum activity and academic performance. To address these aims online discussion forum logs, student marks and teaching delivery method (blended or fully online) data were extracted from the universities learning management system (LMS). Student social network centrality measures were then calculated from the course discussion activity and correlated against student academic performance for each delivery mode. Drawing on social capital and social network theories the analyses identified that in comparison to low performing students the high-performing group held more central positions in their networks and tended to establish dense social connections with students of a similar academic ability. It was also observed that the relationships formed in blended teaching units were of a greater intensity and reciprocity than those established in fully online teaching units indicating a higher level of social capital was reached. This difference in the amount of available social capital between the two teaching modes suggests that students in blended units have comparatively greater access to resources embedded within the network, which in turn can be mobilised to assist them in their academic endeavours.</p>

A Critical Review of Centrality Measures in Social Networks

Social networks are currently gaining increasing impact in the light of the ongoing growth of web-based services like facebook.com. One major challenge for the economically successful implementation of selected management activities such as viral marketing is the identification of key persons with an outstanding structural position within the network. For this purpose, social network analysis provides a lot of measures for quantifying a member’s interconnectedness within social networks. In this context, our paper shows the state of the art with regard to centrality measures for social networks. Due to strongly differing results with respect to the quality of different centrality measures, this paper also aims at illustrating the tremendous importance of a reflected utilization of existing centrality measures. For this purpose, the paper analyzes five centrality measures commonly discussed in literature on the basis of three simple requirements for the behavior of centrality measures.