# Co-citation and Co-authorship Networks of Statisticians

arXiv: https://arxiv.org/abs/2204.11194 · HTML: https://mlsanity.com/paper/2204.11194/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2022.

## Built on by (most cited first, 13 of 13)

- [Spectral Ranking Inferences based on General Multiway Comparisons](https://mlsanity.com/paper/2308.02918/) (2023)
- [A Comparison of DeepSeek and Other LLMs](https://mlsanity.com/paper/2502.03688/) (2025)
- [Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks](https://mlsanity.com/paper/2211.00912/) (2022)
- [Research Topic Flows in Co-Authorship Networks](https://mlsanity.com/paper/2206.07980/) (2022)
- [Semi-supervised Community Detection via Structural Similarity Metrics](https://mlsanity.com/paper/2306.01089/) (2023)
- [Inferences on Mixing Probabilities and Ranking in Mixed-Membership Models](https://mlsanity.com/paper/2308.14988/) (2023)
- [Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification](https://mlsanity.com/paper/2405.16672/) (2024)
- [Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models](https://mlsanity.com/paper/2503.17809/) (2025)
- [Lower Ricci Curvature for Hypergraphs](https://mlsanity.com/paper/2506.03943/) (2025)
- [A Transfer Learning Framework for Multilayer Networks via Model Averaging](https://mlsanity.com/paper/2506.12455/) (2025)
- [How many asymmetric communities are there in multi-layer directed networks?](https://mlsanity.com/paper/2602.21569/) (2026)
- [Quotient-Based Posterior Analysis for Euclidean Latent Space Models](https://mlsanity.com/paper/2604.02739/) (2026)
- [NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity](https://mlsanity.com/paper/2604.11673/) (2026)

## Abstract (arXiv)

We collected and cleaned a large data set on publications in statistics. The data set consists of the coauthor relationships and citation relationships of 83, 331 papers published in 36 representative journals in statistics, probability, and machine learning, spanning 41 years. The data set allows us to construct many different networks, and motivates a number of research problems about the research patterns and trends, research impacts, and network topology of the statistics community. In this paper we focus on (i) using the citation relationships to estimate the research interests of authors, and (ii) using the coauthor relationships to study the network topology. Using co-citation networks we constructed, we discover a "statistics triangle", reminiscent of the statistical philosophy triangle (Efron, 1998). We propose new approaches to constructing the "research map" of statisticians, as well as the "research trajectory" for a given author to visualize his/her research interest evolvement. Using co-authorship networks we constructed, we discover a multi-layer community tree and produce a Sankey diagram to visualize the author migrations in different sub-areas. We also propose several new metrics for research diversity of individual authors. We find that "Bayes", "Biostatistics", and "Nonparametric" are three primary areas in statistics. We also identify 15 sub-areas, each of which can be viewed as a weighted average of the primary areas, and identify several underlying reasons for the formation of co-authorship communities. We also find that the research interests of statisticians have evolved significantly in the 41-year time window we studied: some areas (e.g., biostatistics, high-dimensional data analysis, etc.) have become increasingly more popular.