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The utilization of multi-layer network structures now enables the explanationof complex systems in nature from multiple perspectives. Multi-layer academicnetworks capture diverse relationships among academic entities, facilitatingthe study of academic development and the prediction of future directions.However, there are currently few academic network datasets that simultaneouslyconsider multi-layer academic networks; often, they only include a singlelayer. In this study, we provide a large-scale multi-layer academic networkdataset, namely, LMANStat, which includes collaboration, co-institution,citation, co-citation, journal citation, author citation, author-paper andkeyword co-occurrence networks. Furthermore, each layer of the multi-layeracademic network is dynamic. Additionally, we expand the attributes of nodes,such as authors' research interests, productivity, region and institution.Supported by this dataset, it is possible to study the development andevolution of statistical disciplines from multiple perspectives. This datasetalso provides fertile ground for studying complex systems with multi-layerstructures.

Large-scale Multi-layer Academic Networks Derived from Statistical Publications