Tanmoy Chakraborty

5.1k total citations · 1 hit paper
193 papers, 2.6k citations indexed

About

Tanmoy Chakraborty is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Sociology and Political Science. According to data from OpenAlex, Tanmoy Chakraborty has authored 193 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 116 papers in Artificial Intelligence, 45 papers in Statistical and Nonlinear Physics and 43 papers in Sociology and Political Science. Recurrent topics in Tanmoy Chakraborty's work include Topic Modeling (52 papers), Complex Network Analysis Techniques (44 papers) and Misinformation and Its Impacts (39 papers). Tanmoy Chakraborty is often cited by papers focused on Topic Modeling (52 papers), Complex Network Analysis Techniques (44 papers) and Misinformation and Its Impacts (39 papers). Tanmoy Chakraborty collaborates with scholars based in India, United States and Singapore. Tanmoy Chakraborty's co-authors include Animesh Mukherjee, Niloy Ganguly, Ponnurangam Kumaraguru, Shivangi Singhal, Rajiv Ratn Shah, Md Shad Akhtar, Shin’ichi Satoh, Ayushi Dalmia, V. S. Subrahmanian and Sanjukta Bhowmick and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Tanmoy Chakraborty

180 papers receiving 2.5k citations

Hit Papers

SpotFake: A Multi-modal Framework for Fake News Detection 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. SpotFake: A Multi-modal Framework for Fake News Detection
    2019 289 citations Tanmoy Chakraborty et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tanmoy Chakraborty India 25 1.5k 798 789 609 327 193 2.6k
Srijan Kumar United States 20 889 0.6× 485 0.6× 667 0.8× 434 0.7× 107 0.3× 72 1.9k
John O’Donovan United States 24 924 0.6× 497 0.6× 1.3k 1.6× 231 0.4× 183 0.6× 62 2.3k
Kalina Bontcheva United Kingdom 36 3.1k 2.0× 1.4k 1.8× 1.4k 1.8× 608 1.0× 281 0.9× 160 4.5k
Fábio Crestani Switzerland 27 2.0k 1.3× 441 0.6× 1.6k 2.0× 225 0.4× 422 1.3× 238 3.3k
Derek Greene Ireland 20 1.1k 0.7× 382 0.5× 515 0.7× 531 0.9× 168 0.5× 94 2.3k
Aron Culotta United States 30 2.0k 1.3× 696 0.9× 578 0.7× 351 0.6× 123 0.4× 72 3.3k
Qi He United States 21 1.9k 1.3× 424 0.5× 1.7k 2.2× 1.3k 2.1× 294 0.9× 64 3.6k
Petr Sojka Czechia 11 1.7k 1.1× 298 0.4× 494 0.6× 128 0.2× 139 0.4× 81 2.7k
Nitin Agarwal United States 22 581 0.4× 558 0.7× 571 0.7× 678 1.1× 60 0.2× 157 2.0k
Min Zhang China 34 2.3k 1.5× 339 0.4× 2.8k 3.5× 183 0.3× 155 0.5× 181 4.0k

Countries citing papers authored by Tanmoy Chakraborty

Since Specialization
Citations

This map shows the geographic impact of Tanmoy Chakraborty'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 Tanmoy Chakraborty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tanmoy Chakraborty more than expected).

Fields of papers citing papers by Tanmoy Chakraborty

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tanmoy Chakraborty. 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 Tanmoy Chakraborty. The network helps show where Tanmoy Chakraborty may publish in the future.

Co-authorship network of co-authors of Tanmoy Chakraborty

This figure shows the co-authorship network connecting the top 25 collaborators of Tanmoy Chakraborty. A scholar is included among the top collaborators of Tanmoy Chakraborty 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 Tanmoy Chakraborty. Tanmoy Chakraborty is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chakraborty, Tanmoy, et al.. (2025). Hybrid quantum repeaters with ensemble-based quantum memories and single-spin photon transducers. npj Quantum Information. 11(1).
2.
Sharma, Divya, Neha Jha, Apoorva Gupta, et al.. (2024). Literature mining discerns latent disease–gene relationships. Bioinformatics. 40(4). 2 indexed citations
3.
Chakraborty, Tanmoy, et al.. (2024). LM2: A Simple Society of Language Models Solves Complex Reasoning. 16473–16484. 1 indexed citations
4.
Akhtar, Md Shad, et al.. (2024). Emotion-Aware Multimodal Fusion for Meme Emotion Detection. IEEE Transactions on Affective Computing. 15(3). 1800–1811. 3 indexed citations
5.
Patel, Divya, et al.. (2024). Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!. 16820–16842. 1 indexed citations
6.
Mondal, Ishani, et al.. (2023). Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 12986–12994. 7 indexed citations
7.
Chakraborty, Tanmoy, et al.. (2023). Judging the creative prowess of AI. Nature Machine Intelligence. 5(6). 558–558. 3 indexed citations
8.
9.
Chakraborty, Tanmoy, et al.. (2023). Node injection for class-specific network poisoning. Neural Networks. 166. 236–247. 12 indexed citations
10.
Akhtar, Md Shad, et al.. (2022). Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis. 792–805. 2 indexed citations
11.
Pramanick, Shraman, Dimitar Dimitrov, Shivam Sharma, et al.. (2021). Detecting Harmful Memes and Their Targets. 2783–2796. 58 indexed citations
12.
Singh, Richa, et al.. (2020). NewsBag: A Benchmark Multimodal Dataset for Fake News Detection.. National Conference on Artificial Intelligence. 138–145. 5 indexed citations
13.
Sharma, Chhavi, William G. Scott, Srinivas Pykl, et al.. (2020). SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!. 759–773. 109 indexed citations
14.
Chakraborty, Tanmoy, et al.. (2019). Spotting Collusive Behaviour of Online Fraud Groups in Customer Reviews.. arXiv (Cornell University). 4 indexed citations
15.
Chakraborty, Tanmoy, et al.. (2018). ComPAS: Community Preserving Sampling for Streaming Graphs. Adaptive Agents and Multi-Agents Systems. 184–192. 1 indexed citations
16.
Chakraborty, Tanmoy, Fabio Pierazzi, & V. S. Subrahmanian. (2017). EC2: Ensemble Clustering and Classification for Predicting Android Malware Families. IEEE Transactions on Dependable and Secure Computing. 17(2). 262–277. 83 indexed citations
17.
Chakraborty, Tanmoy & Prasenjit Choudhury. (2016). Authorship identification in Bengali language: a graph based approach. 443–446. 4 indexed citations
18.
Chakraborty, Tanmoy, et al.. (2014). Towards a stratified learning approach to predict future citation counts. 351–360. 45 indexed citations
19.
Chakraborty, Tanmoy. (2012). Authorship Identification Using Stylometry Analysis in Bengali Literature. arXiv (Cornell University). 8 indexed citations
20.
Chakraborty, Tanmoy, Dipankar Das, & Sivaji Bandyopadhyay. (2011). Semantic Clustering: an Attempt to Identify Multiword Expressions in Bengali. Meeting of the Association for Computational Linguistics. 8–13. 3 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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