Co-citation and Co-authorship Networks of Statisticians

Ji, Pengsheng; Jin, Jiashun; Ke, Zheng Tracy; Li, Wan-Shan

doi:10.1080/07350015.2021.1978469

Cited by 35 publications

(53 citation statements)

References 26 publications

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“…. , 15 (say) and see which K gives the most reasonable results (e.g., Ji et al (2021a)). For either of the two normalization approaches, let R (5) , R (6) , .…”

Section: Other Score-type Post-pca Normalization Approachesmentioning

confidence: 99%

“…This is motivated by the observation that for assortative networks, a negative eigenvalue is more likely to be spurious than a positive eigenvalue. Ji et al (2021a) found that if we use the modified SCORE with c 0 = 1, then the estimated communities are assortative (from a practical viewpoint, such a result is more appealing). For network analysis where we encounter a similar issue, we may use the modified SCORE, with the same or a different tuning parameter c 0 .…”

Section: Score* An Improvement Of Score For Assortative Networkmentioning

confidence: 99%

“…We then continue with this until we decide none of the communities is further dividable. Ji et al (2021a) used the algorithm to build a 4-layer tree with 26 leaf communities for the co-authorship network of statisticians, where they set α 0 = 0.001, and determine the number of communities in each layer by combining the scree plot and the authors' partial knowledge of the statistics community. See Section 2.10 and Ji et al (2021a) for a more detailed discussion.…”

Section: Hier-score: Score For Hierarchical Network Community Detectionmentioning

confidence: 99%

“…We start with the network where each author in the data range is a node, and two nodes have an edge if and only if they have co-authored k or more papers in the data range. If we choose k = 1 or 2, the network is quite large and hard to analyze, so Ji et al (2021a) took k = 3. The network is comparably fragmented.…”

Section: Community Detection By D-score For Directed Networkmentioning

confidence: 99%

See 3 more Smart Citations

The SCORE normalization, especially for highly heterogeneous network and text data

Ke¹,

Jin²

2022

Preprint

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SCORE was introduced as a spectral approach to network community detection. Since many networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) approach to community detection may perform unsatisfactorily. SCORE alleviates the effect of degree heterogeneity by introducing a new normalization idea in the spectral domain and makes OSC more effective. SCORE is easy to use and computationally fast. It adapts easily to new directions and sees an increasing interest in practice. In this paper, we review the basics of SCORE, the adaption of SCORE to network mixed membership estimation and topic modeling, and the application of SCORE in real data, including two datasets on the publications of statisticians. We also review the theoretical 'ideology' underlying SCORE. We show that in the spectral domain, SCORE converts a simplicial cone to a simplex, and provides a simple and direct link between the simplex and network memberships. SCORE attains an exponential rate and a sharp phase transition in community detection, and achieves optimal rates in mixed membership estimation and topic modeling.

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“…. , 15 (say) and see which K gives the most reasonable results (e.g., Ji et al (2021a)). For either of the two normalization approaches, let R (5) , R (6) , .…”

Section: Other Score-type Post-pca Normalization Approachesmentioning

confidence: 99%

Section: Score* An Improvement Of Score For Assortative Networkmentioning

confidence: 99%

Section: Hier-score: Score For Hierarchical Network Community Detectionmentioning

confidence: 99%

Section: Community Detection By D-score For Directed Networkmentioning

confidence: 99%

See 2 more Smart Citations

The SCORE normalization, especially for highly heterogeneous network and text data

Ke¹,

Jin²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…, π n from the data matrix A. This problem has found applications in learning research interests of statisticians from co-citation networks Ji et al (2022) and understanding developmental brain disorders from gene co-expression networks Liu et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Optimal Network Membership Estimation Under Severe Degree Heterogeneity

Ke¹,

Wang²

2022

Preprint

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Real networks often have severe degree heterogeneity. We are interested in studying the effect of degree heterogeneity on estimation of the underlying community structure. We consider the degree-corrected mixed membership model (DCMM) for a symmetric network with n nodes and K communities, where each node i has a degree parameter θ i and a mixed membership vector π i . The level of degree heterogeneity is captured by F n (•) -the empirical distribution associated with n (scaled) degree parameters. We first show that the optimal rate of convergence for the 1 -loss of estimating π i 's depends on an integral with respect to F n (•). We call a method optimally adaptive to degree heterogeneity (in short, optimally adaptive) if it attains the optimal rate for arbitrary F n (•). Unfortunately, none of the existing methods satisfy this requirement. We propose a new spectral method that is optimally adaptive, the core idea behind which is using a pre-PCA normalization to yield the optimal signal-to-noise ratio simultaneously at all entries of each leading empirical eigenvector. As one technical contribution, we derive a new row-wise large-deviation bound for eigenvectors of the regularized graph Laplacian.

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Community detection in attributed collaboration network for statisticians

Zhang

Pan

Wang

et al. 2023

Stat

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Scientific collaboration helps to promote the dissemination of knowledge and is essential in breeding innovation. Collaboration network analysis is a useful tool to study researchers' collaborations. In this work, we collect papers published between 2001 and 2018 in 43 statistical journals and investigate the collaborative trends and patterns. We find that more and more researchers take part in statistical research, and cooperation among them is strengthening. We further construct an attributed collaboration network and extract its core. Community detection is conducted on the core network by using the edge cross‐validation (ECV) method and the attributed network clustering algorithm (ANCA). In particular, we extend the ANCA to deal with networks having both categorical and continuous attributes. Influential researchers are identified in each community. Furthermore, two kinds of homophily are revealed in our collaboration network: research topic homophily and spatial proximity homophily. Based on the homophily and transitivity, we can make recommendations for researchers. Finally, we compare ANCA with the other three methods and confirm that the combination of nodal attributes and network structure improves the quality of community detection. Our studies show the features of the collaboration among statisticians and present a new perspective to explore researchers.

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Co-citation and Co-authorship Networks of Statisticians

Cited by 35 publications

References 26 publications

The SCORE normalization, especially for highly heterogeneous network and text data

The SCORE normalization, especially for highly heterogeneous network and text data

Optimal Network Membership Estimation Under Severe Degree Heterogeneity

Community detection in attributed collaboration network for statisticians

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