Coauthorship and Citation Networks for Statisticians

Abstract

We have collected and cleaned two network data sets: Coauthorship and Citation networks for statisticians. The data sets are based on all research papers published in four of the top journals in statistics from $2003$ to the first half of $2012$. We analyze the data sets from many different perspectives, focusing on (a) centrality, (b) community structures, and (c) productivity, patterns and trends. For (a), we have identified the most prolific/collaborative/highly cited authors. We have also identified a handful of "hot" papers, suggesting "Variable Selection" as one of the "hot" areas. For (b), we have identified about $15$ meaningful communities or research groups, including large-size ones such as "Spatial Statistics", "Large-Scale Multiple Testing", "Variable Selection" as well as small-size ones such as "Dimensional Reduction", "Objective Bayes", "Quantile Regression", and "Theoretical Machine Learning". For (c), we find that over the 10-year period, both the average number of papers per author and the fraction of self citations have been decreasing, but the proportion of distant citations has been increasing. These suggest that the statistics community has become increasingly more collaborative, competitive, and globalized. Our findings shed light on research habits, trends, and topological patterns of statisticians. The data sets provide a fertile ground for future researches on or related to social networks of statisticians.

References in corpus (13)

• Modularity and community structure in networks
• Stochastic blockmodels and community structure in networks
• Community structure in directed networks
• Regularized estimation of large covariance matrices
• Covariance regularization by thresholding
• One-step sparse estimates in nonconcave penalized likelihood models
• Mixture models and exploratory analysis in networks
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Size reduction of complex networks preserving modularity
• Fast community detection by SCORE
• Pseudo-likelihood methods for community detection in large sparse networks
• Coauthorship and citation in scientific publishing
• Inversion method for content-based networks

Cited by in corpus (43)

• A Review of Stochastic Block Models and Extensions for Graph Clustering
• Community Detection for Hypergraph Networks via Regularized Tensor Power Iteration
• Testing for Global Network Structure Using Small Subgraph Statistics
• Reciprocity, community detection, and link prediction in dynamic networks
• Improvements on SCORE, Especially for Weak Signals
• Generative model for reciprocity and community detection in networks
• Network cross-validation by edge sampling
• Spectral Algorithms for Community Detection in Directed Networks
• Factor Models for High-Dimensional Tensor Time Series
• Hierarchical community detection by recursive partitioning
• Randomized spectral co-clustering for large-scale directed networks
• A useful criterion on studying consistent estimation in community detection
• Linear regression and its inference on noisy network-linked data
• Estimating the number of communities in weighted networks
• Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
• An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks
• Community models for networks observed through edge nominations
• Informative core identification in complex networks
• Community Detection in General Hypergraph via Graph Embedding
• Directed mixed membership stochastic blockmodel
• Reproducible Science with LaTeX
• A Tutorial on Libra: R package for the Linearized Bregman Algorithm in High Dimensional Statistics
• Directed degree corrected mixed membership model and estimating community memberships in directed networks
• Root and community inference on the latent growth process of a network
• A Social Network Analysis of Articles on Social Network Analysis
• Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks
• Impact of regularization on spectral clustering under the mixed membership stochastic block model
• Network-Assisted Estimation for Large-dimensional Factor Model with Guaranteed Convergence Rate Improvement
• Overlapping community detection in networks via sparse spectral decomposition
• Measuring Research Interest Similarity with Transition Probabilities
• Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition
• A Bayesian Nonparametric Stochastic Block Model for Directed Acyclic Graphs
• Graph matching beyond perfectly-overlapping Erdős--Rényi random graphs
• Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model
• A two-stage working model strategy for network analysis under Hierarchical Exponential Random Graph Models
• Learning to sample fibers for goodness-of-fit testing
• Co-embedding of Nodes and Edges with Graph Neural Networks
• Co-factor analysis of citation networks
• Principal component analysis for high-dimensional compositional data
• High-dimensional Gaussian graphical model for network-linked data
• An improved spectral clustering method for mixed membership community detection
• Tractably Modelling Dependence in Networks Beyond Exchangeability
• A Geometrical Approach to Topic Model Estimation