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Register a new userOn the relationship between the structural and socioacademic communities of a coauthorship network
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This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of a multi-institution, interdisciplinary research group focused on the study of sensor networks and wireless communication. Four different community detection algorithms were employed to assign a structural community to each scholar in the network: leading eigenvector, walktrap, edge betweenness and spinglass. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearsons $chi^2$ test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by departmental and institutional affiliation.
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Community detection techniques are widely used to infer hidden structures within interconnected systems. Despite demonstrating high accuracy on benchmarks, they reproduce the external classification for many real-world systems with a significant level of discrepancy. A widely accepted reason behind such outcome is the unavoidable loss of non-topological information (such as node attributes) encountered when the original complex system is represented as a network. In this article we emphasize that the observed discrepancies may also be caused by a different reason: the external classification itself. For this end we use scientific publication data which i) exhibit a well defined modular structure and ii) hold an expert-made classification of research articles. Having represented the articles and the extracted scientific concepts both as a bipartite network and as its unipartite projection, we applied modularity optimization to uncover the inner thematic structure. The resulting clusters are shown to partly reflect the author-made classification, although some significant discrepancies are observed. A detailed analysis of these discrepancies shows that they carry essential information about the system, mainly related to the use of similar techniques and methods across different (sub)disciplines, that is otherwise omitted when only the external classification is considered.
Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, all the current detection algorithms are motivated by the practical problems, whose applicabilities in other fields are open to question. Thence, for a new community problem, researchers need to derive algorithms ad hoc, which is arduous and even unnecessary. In this paper, we represent a general procedure to find community structures in practice. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.
Tracing the evolution of specific topics is a subject area which belongs to the general problem of mapping the structure of scientific knowledge. Often bibliometric data bases are used to study the history of scientific topic evolution from its appearance to its extinction or merger with other topics. In this chapter the authors present an analysis of the academic response to the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. Using a bibliographic database the distributions of Chornobyl-related papers in different scientific fields are analysed, as are their growth rates and properties of co-authorship networks. Elements of descriptive statistics and tools of complex-network theory are used to highlight interdisciplinary as well as international effects. In particular, tools of complex-network science enable information visualization complemented by further quantitative analysis. A further goal of the chapter is to provide a simple pedagogical introduction to the application of complex-network analysis for visual data representation and interdisciplinary communication.
Computing such correlation coefficient would be straightforward had we had available the rankings given by the prize committee to all scientists in the pool. In reality we only have citation rankings for all scientists. This means, however, that we have the ordinal rankings of the prize winners with regard to citation metrics. I use maximum likelihood method to infer the most probable correlation coefficient to produce the observed pattern of ordinal ranks of the prize winners. I get the correlation coefficients of 0.47 and 0.59 between the composite citation indicator and getting Abel Prize and Fields Medal, respectively. The correlation coefficient between getting a Nobel Prize and the Q-factor is 0.65. These coefficients are of the same magnitude as the correlation coefficient between Elo ratings of the chess players and their popularity measured as numbers of webpages mentioning the players.
This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.
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