Abstract
Scholarly network analysis is a fundamental topic in academia domain, which is beneficial for estimating the contribution of researchers and the quality of academic outputs. Recently, a popular fashion takes advantage of network embedding techniques, which aims to learn the scholarly information into vectorial representations for the task. Though great progress has been made, existing studies only consider the text information of papers for scholarly network representation, while ignoring the effects of many intrinsic and informative features, especially the different influences and contribution of authors and cooperations. In order to alleviate this problem, in this paper, we propose a novel Author Contributed Representation for Scholarly Network (ACR-SN) framework to learn the unique representation for scholarly networks, which characterizes the different authors’ contribution. Specifically, we first adopt a graph convolutional network (GCN) to capture the structure information in the citation network. Then, we calculate the correlations between authors and each paper, and aggregate each embedding of authors according to their contribution by using the attention mechanism. Extensive experiments on two real world datasets demonstrate the effectiveness of ACR-SN and reveal that authors’ contribution to the paper varies with the corresponding authorities and interested fields.
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Acknowledgements
This work was supported in part by National Natural Science Foundation of China (Grant No. 61703386), the Anhui Sun Create Electronics Company Ltd., under Grant KD1809300321, in part by the National Key R&D Program of China under Grant 2018YFC0832101, and in part by the National Key New Product Plan of China under Grant 2014GRC30006.
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Wang, B. et al. (2020). Author Contributed Representation for Scholarly Network. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_41
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DOI: https://doi.org/10.1007/978-3-030-60259-8_41
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