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

TL;DR

This paper introduces LMANStat, a large-scale multi-layer academic network dataset capturing diverse relationships among statistical researchers and publications, enabling comprehensive analysis of discipline development and complex systems.

Contribution

The paper presents a novel, extensive multi-layer academic network dataset with dynamic layers and enriched node attributes, filling a gap in existing datasets for multi-layer network research.

Findings

Provides a comprehensive dataset for multi-layer academic network analysis

Enables study of statistical discipline evolution from multiple perspectives

Supports research on complex systems with multi-layer structures

Abstract

The utilization of multi-layer network structures now enables the explanation of complex systems in nature from multiple perspectives. Multi-layer academic networks capture diverse relationships among academic entities, facilitating the study of academic development and the prediction of future directions. However, there are currently few academic network datasets that simultaneously consider multi-layer academic networks; often, they only include a single layer. In this study, we provide a large-scale multi-layer academic network dataset, namely, LMANStat, which includes collaboration, co-institution, citation, co-citation, journal citation, author citation, author-paper and keyword co-occurrence networks. Furthermore, each layer of the multi-layer academic 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 and evolution of statistical disciplines from multiple perspectives. This dataset also provides fertile ground for studying complex systems with multi-layer structures.