Centrality maps and the analysis of city street networks
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Firstly introduced in social science, the notion of centrality has spread to the whole complex network science. A centrality is a measure that quanti es whether an element of a network is well served or not, easy to reach, necessary to cross. This article focuses on cities' street network (seenKeywords:
Katz centrality
Social Network Analysis
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Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.
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Background: In network science, although different types of centrality measures have been introduced to determine important nodes of networks, a consensus pipeline to select and implement the best-tailored measure for each complex network is still an open field. In the present study, we examine the node centrality profiles of protein-protein interaction networks (PPINs) in order to detect which measure is succeeding to predict influential proteins. We study and demonstrate the effects of inherent topological features and network reconstruction approaches on the centrality measure values. Results: PPINs were used to compare a large number of well-known centrality measures. Unsupervised machine learning approaches, including principal component analysis (PCA) and clustering methods, were applied to find out how these measures are similar in terms of characterizing and assorting network influential constituents. We found that the principle components of the network centralities and the contribution level of them demonstrated a network-dependent significance of these measures. We show that some centralities namely Latora, Decay, Lin, Freeman, Diffusion, Residual and Average had a high level of information in comparison with other measures in all PPINs. Finally, using clustering analysis, we restated that the determination of important nodes within a network depends on its topology. Conclusions: Using PCA and identifying the contribution proportion of the variables, i.e., centrality measures in principal components, is a prerequisite step of network analysis in order to infer any functional consequences, e.g., the essentiality of a node. Our conclusion is based on the signal and noise modeling using PCA and the similarity distance between clusters. Also, an interesting strong correlation between silhouette criterion and contribution value was found which corroborates our results.
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Abstract This article reviews developments in network analysis and its applications to problems of organization. From an arcane social science tradition focused chiefly on methods, it has evolved into a major paradigm for thinking about and researching relationships and social structure within and among organizations. Network ideas and methods have been embraced by organizational theorists in part because the world of organizations has itself changed: from centrally coordinated hierarchies to loosely linked strategic alliances and “network forms.” Methods issues continue to be important in network analysis; however, the data and the statistical methods appropriate to network study are quite different from those of other social and behavioral science domains. Network methods and models at varying levels of analysis are reviewed – node, dyad, subnetwork (clique or cluster), and network – and how they have been applied to recent subfields of organizational study.
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In recent years, social network theory becomes more and more significant in social science. Basing on the fast-growing social network theory, SNA (Social Network Analysis) is also widely used and published in different journals. As social actors are like nodes in the network, we use centrality to measure these nodes in power, activity and communication convenience etc.. Degree centrality, betweenness centrality and closeness centrality are main detailed measurement, and they have different algorithm. In SNA study, the research purpose determines the selection of centrality; and the use of these three centralities constitutes an important part in SNA study.
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최근 일부 계량서지학 연구에서 사회 네트워크 분석 분야의 도구인 중심성 분석 기법을 도입하려는 시도가 나타나고 있다. 그런데 사회 네트워크를 대상으로 개발된 중심성 척도는 노드간 연결에 대한 가중치를 반영하지 않는 단점이 있다 이 연구에서는 가중 네트워크인 계량서지적 자료에 대해서 중심성을 분석할 수 있는 새로운 척도를 네 가지 제시하였다. 제안된 척도의 유용성을 검증하기 위해서 저자동시인용 네트워크. 용어 동시출현 네트워크, 웹사이트 동시링크 네트워크의 세 가지 실제 자료에 대해서 중심성 분석을 수행하였다. 분석 결과에서는 제안된 중심성 척도가 계량서지적 네트워크를 대상으로 개별 노드의 입지와 영향력을 파악하는데 있어서 매우 유용함이 확인되었다 Recently, some bibliometric researchers tried to use the centrality analysis methods and the centrality measures which are standard tools in social network analysis. However the traditional centrality measures originated from social network analysis could not deal with weighted networks such as co-citation networks. In this study. new centrality measures for analyzing bibliometric networks with link weights are suggested and applied to three real network data, including an author co-citation network, a co-word network, and a website co-link network. The results of centrality analyses in these three cases can be regarded as Promising the usefulness of suggested centrality measures, especially in analyzing the Position and influence of each node in a bibliometric network.
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