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Diversity Index of Academic Community Ecosystem by Co-authorship Analysis with Granger Causality

机译:GRANGER因果关系共同作者分析学术社区生态系统的多样性指标

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Compared to individual-level analysis, community-level analysis provides a new perspective to inspect network structure. It focuses on modeling the evolving relationships between communities. Intuitively, community-level analysis is a generalization of individual-level analysis. It reflects a macroscopic evolution of a network and reduces the overfitting of individual analysis to some degree. In this paper, we investigate the co-authorship characteristics between different affiliations in academic social networks and then adopt the weighted multigraph model to establish the coauthorships between communities. Subsequently, we define the Co-authorship Factor (CF) for each pair of communities and then propose the modified Shannon Co-authorship Diversity Index (SCDI) and Renyi Co-authorship Diversity Index (RCDI) to measure the diversity of co-authorship ecosystem of a certain community. Finally, we apply the Granger causality to model the mutual co-authorship influences between communities along time. We verify our proposed indexes on real dataset which is mainly based on the DBLP and Microsoft Academic Graph (MAG) datasets.
机译:与个体级别分析相比,社区级别分析提供了对网络结构的新视角。它侧重于建模社区之间的不断发展的关系。直观地,社区级别分析是个人级别分析的概括。它反映了网络的宏观演化,并减少了各个分析的过度达到某种程度。在本文中,我们调查了学术社交网络不同隶属关系之间的共同作者特征,然后采用加权多金属模型来建立社区之间的共同驻建。随后,我们为每对社区定义共同作者因素(CF),然后提出修改的Shannon共同作者分集指数(SCDI)和瑞尼的共同作者分集指数(RCDI),以衡量共同作者生态系统的多样性某个社区。最后,我们应用格兰杰因果关系来模拟沿着时间之间社区之间的互相共同作者的影响。我们验证了我们在实际数据集上的建议索引,主要基于DBLP和Microsoft学术图(MAG)数据集。

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