Purnamrita Sarkar

2.1k total citations
35 papers, 1.1k citations indexed

About

Purnamrita Sarkar is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Statistics and Probability. According to data from OpenAlex, Purnamrita Sarkar has authored 35 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 16 papers in Statistical and Nonlinear Physics and 8 papers in Statistics and Probability. Recurrent topics in Purnamrita Sarkar's work include Complex Network Analysis Techniques (16 papers), Bayesian Methods and Mixture Models (7 papers) and Statistical Methods and Inference (6 papers). Purnamrita Sarkar is often cited by papers focused on Complex Network Analysis Techniques (16 papers), Bayesian Methods and Mixture Models (7 papers) and Statistical Methods and Inference (6 papers). Purnamrita Sarkar collaborates with scholars based in United States, Australia and United Kingdom. Purnamrita Sarkar's co-authors include Andrew Moore, Michael I. Jordan, Ariel Kleiner, Ameet Talwalkar, Deepayan Chakrabarti, Beth Trushkowsky, Tim Kraska, Michael J. Franklin, Andrew W. Moore and Amit Prakash and has published in prestigious journals such as Journal of the American Statistical Association, Communications of the ACM and Biometrika.

In The Last Decade

Purnamrita Sarkar

32 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Purnamrita Sarkar United States	15	536	496	158	140	134	35	1.1k
Hisashi Kashima Japan	12	563 1.1×	143 0.3×	48 0.3×	65 0.5×	234 1.7×	28	1.0k
Tossapon Boongoen Thailand	18	615 1.1×	139 0.3×	48 0.3×	111 0.8×	286 2.1×	59	994
Christian Posse United States	17	255 0.5×	147 0.3×	91 0.6×	91 0.7×	135 1.0×	39	792
Natthakan Iam-On Thailand	16	629 1.2×	153 0.3×	32 0.2×	105 0.8×	336 2.5×	51	966
Mohamed Nadif France	20	903 1.7×	233 0.5×	81 0.5×	38 0.3×	329 2.5×	84	1.3k
Hung T. Nguyen United States	13	433 0.8×	400 0.8×	137 0.9×	138 1.0×	30 0.2×	39	979
Hwanjo Yu South Korea	16	731 1.4×	172 0.3×	18 0.1×	150 1.1×	169 1.3×	42	1.1k
Nina Mishra United States	18	839 1.6×	118 0.2×	19 0.1×	233 1.7×	105 0.8×	37	1.2k
Subramanyam Mallela United States	8	1.0k 1.9×	279 0.6×	37 0.2×	179 1.3×	477 3.6×	9	1.5k
Adrian Vetta Canada	14	279 0.5×	269 0.5×	20 0.1×	392 2.8×	128 1.0×	48	1.4k

Countries citing papers authored by Purnamrita Sarkar

Since Specialization

Citations

This map shows the geographic impact of Purnamrita Sarkar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Purnamrita Sarkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Purnamrita Sarkar more than expected).

Fields of papers citing papers by Purnamrita Sarkar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Purnamrita Sarkar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Purnamrita Sarkar. The network helps show where Purnamrita Sarkar may publish in the future.

Co-authorship network of co-authors of Purnamrita Sarkar

This figure shows the co-authorship network connecting the top 25 collaborators of Purnamrita Sarkar. A scholar is included among the top collaborators of Purnamrita Sarkar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Purnamrita Sarkar. Purnamrita Sarkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Y. X. Rachel, et al.. (2022). A Unified Framework for Tuning Hyperparameters in Clustering Problems. Statistica Sinica.

Chakrabarti, Deepayan, et al.. (2021). Consistent Nonparametric Methods for Network Assisted Covariate Estimation. International Conference on Machine Learning. 7435–7446. 1 indexed citations

Sarkar, Purnamrita, et al.. (2021). When random initializations help: a study of variational inference for community detection. Journal of Machine Learning Research. 22(22). 1–46. 1 indexed citations

Sarkar, Purnamrita, et al.. (2020). On hyperparameter tuning in general clustering problemsm. International Conference on Machine Learning. 1. 2996–3007.

Li, Tianxi, Lihua Lei, Sharmodeep Bhattacharyya, et al.. (2020). Hierarchical Community Detection by Recursive Partitioning. Journal of the American Statistical Association. 117(538). 951–968. 37 indexed citations

Sarkar, Purnamrita, et al.. (2018). Overlapping Clustering Models, and One (class) SVM to Bind Them All. Neural Information Processing Systems. 31. 2126–2136. 5 indexed citations

Sarkar, Purnamrita, et al.. (2018). Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues. Neural Information Processing Systems. 31. 10694–10704. 4 indexed citations

Yan, Bowei, et al.. (2017). Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models. arXiv (Cornell University). 6798–6808.

Yan, Bowei, Purnamrita Sarkar, & Xiuyuan Cheng. (2017). Exact Recovery of Number of Blocks in Blockmodels. arXiv (Cornell University). 2 indexed citations

10.

Yan, Bowei, et al.. (2017). Convergence of Gradient EM on Multi-component Mixture of Gaussians. Neural Information Processing Systems. 30. 6956–6966. 12 indexed citations

11.

Yan, Bowei & Purnamrita Sarkar. (2016). On Robustness of Kernel Clustering. Neural Information Processing Systems. 29. 3090–3098. 5 indexed citations

12.

Sarkar, Purnamrita, Deepayan Chakrabarti, & Peter J. Bickel. (2015). The consistency of common neighbors for link prediction in stochastic blockmodels. Neural Information Processing Systems. 28. 3016–3024. 4 indexed citations

13.

Kleiner, Ariel, Ameet Talwalkar, Purnamrita Sarkar, & Michael I. Jordan. (2014). A Scalable Bootstrap for Massive Data. Journal of the Royal Statistical Society Series B (Statistical Methodology). 76(4). 795–816. 218 indexed citations

14.

Rout, Sabyasachi, Ajay Kumar, Purnamrita Sarkar, Manish Mishra, & P. M. Ravi. (2013). Application of Chemometric methods for assessment of heavy metal pollution and source apportionment in Riparian zone soil of Ulhas River estuary, India. International Journal on Environmental Sciences. 3(5). 1485–1496. 13 indexed citations

15.

Sarkar, Purnamrita, Deepayan Chakrabarti, & Michael I. Jordan. (2011). Non-parametric Link Prediction. arXiv (Cornell University). 1 indexed citations

16.

Sarkar, Purnamrita, Deepayan Chakrabarti, & Andrew Moore. (2010). Theoretical Justification of Popular Link Prediction Heuristics.. Conference on Learning Theory. 2722–2727. 26 indexed citations

17.

Sarkar, Purnamrita, Andrew W. Moore, & Amit Prakash. (2008). Fast incremental proximity search in large graphs. 896–903. 61 indexed citations

18.

Sarkar, Purnamrita, Sajid M. Siddiqi, & Geoffrey J. Gordon. (2007). A Latent Space Approach to Dynamic Embedding of Co-occurrence Data. International Conference on Artificial Intelligence and Statistics. 420–427. 41 indexed citations

19.

Sarkar, Purnamrita & Andrew Moore. (2007). A tractable approach to finding closest truncated-commute-time neighbors in large graphs. Uncertainty in Artificial Intelligence. 335–343. 50 indexed citations

20.

Sarkar, Purnamrita & Andrew Moore. (2005). Dynamic Social Network Analysis using Latent Space Models. Neural Information Processing Systems. 18. 1145–1152. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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