Mainak Chatterjee

2.7k total citations · 1 hit paper
26 papers, 1.9k citations indexed

About

Mainak Chatterjee is a scholar working on Computational Theory and Mathematics, Computer Networks and Communications and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Mainak Chatterjee has authored 26 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 8 papers in Computer Networks and Communications and 5 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Mainak Chatterjee's work include Computational Drug Discovery Methods (14 papers), Pesticide Residue Analysis and Safety (5 papers) and Environmental Toxicology and Ecotoxicology (5 papers). Mainak Chatterjee is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Pesticide Residue Analysis and Safety (5 papers) and Environmental Toxicology and Ecotoxicology (5 papers). Mainak Chatterjee collaborates with scholars based in India, United States and Sweden. Mainak Chatterjee's co-authors include Sajal K. Das, Damla Turgut, Kunal Roy, P. Venkata Krishna, Nitin H. Vaidya, Dhiraj Pradhan, Arkaprava Banerjee, Priyanka De, Dhiraj K. Pradhan and Agnieszka Gajewicz and has published in prestigious journals such as Journal of Hazardous Materials, Journal of Agricultural and Food Chemistry and Chemosphere.

In The Last Decade

Mainak Chatterjee

25 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

2002 925 citations Mainak Chatterjee, Sajal K. Das et al. Cluster Computing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mainak Chatterjee India	14	1.4k	531	259	80	75	26	1.9k
Yumei Sun China	18	454 0.3×	59 0.1×	540 2.1×	72 0.9×	34 0.5×	53	1.8k
Guochuan Zhang China	18	505 0.4×	60 0.1×	91 0.4×	21 0.3×	16 0.2×	100	1.3k
Xiang Cai China	18	557 0.4×	112 0.2×	60 0.2×	125 1.6×	66 0.9×	56	2.2k
Lu Bai China	10	238 0.2×	85 0.2×	269 1.0×	46 0.6×	229 3.1×	23	1.2k
George Papadopoulos Greece	20	398 0.3×	347 0.7×	38 0.1×	37 0.5×	7 0.1×	128	1.5k
Hailong Yang China	20	1.0k 0.7×	187 0.4×	34 0.1×	49 0.6×	6 0.1×	161	1.8k
Chang Joo Lee South Korea	27	1.2k 0.9×	197 0.4×	10 0.0×	136 1.7×	75 1.0×	92	2.6k
Randy Allen United States	6	427 0.3×	48 0.1×	92 0.4×	16 0.2×	29 0.4×	13	850
Erdal Kılıç Türkiye	16	182 0.1×	126 0.2×	58 0.2×	35 0.4×	14 0.2×	76	969
Durga Prasad Mohapatra India	23	366 0.3×	141 0.3×	20 0.1×	55 0.7×	202 2.7×	178	2.2k

Countries citing papers authored by Mainak Chatterjee

Since Specialization

Citations

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

Fields of papers citing papers by Mainak Chatterjee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mainak Chatterjee

This figure shows the co-authorship network connecting the top 25 collaborators of Mainak Chatterjee. A scholar is included among the top collaborators of Mainak Chatterjee 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 Mainak Chatterjee. Mainak Chatterjee 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

Chatterjee, Mainak, et al.. (2024). Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305. Journal of Hazardous Materials. 479. 135725–135725. 9 indexed citations

Chatterjee, Mainak, et al.. (2024). Intelligent consensus-based predictions of early life stage toxicity in fish tested in compliance with OECD Test Guideline 210. Aquatic Toxicology. 279. 107216–107216.

Banerjee, Arkaprava, et al.. (2024). QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments. Environmental Science Nano. 11(10). 4196–4210. 4 indexed citations

Chatterjee, Mainak, et al.. (2023). Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species. Aquatic Toxicology. 265. 106776–106776. 10 indexed citations

Chatterjee, Mainak, Arkaprava Banerjee, Simone Tosi, et al.. (2023). Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. Journal of Hazardous Materials. 460. 132358–132358. 29 indexed citations

Chatterjee, Mainak & Kunal Roy. (2023). “Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species. Journal of Hazardous Materials. 459. 132129–132129. 17 indexed citations

Chatterjee, Mainak & Kunal Roy. (2023). Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches. Environmental Science Processes & Impacts. 26(1). 105–118. 10 indexed citations

Chatterjee, Mainak & Kunal Roy. (2022). Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. Chemosphere. 308(Pt 3). 136463–136463. 17 indexed citations

Paul, Rahul, Mainak Chatterjee, & Kunal Roy. (2022). First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across. Environmental Science and Pollution Research. 29(58). 88302–88317. 15 indexed citations

10.

Chatterjee, Mainak & Kunal Roy. (2022). Computational Modeling of Mixture Toxicity. Methods in molecular biology. 2425. 561–587. 10 indexed citations

11.

Chatterjee, Mainak, Arkaprava Banerjee, Priyanka De, Agnieszka Gajewicz, & Kunal Roy. (2021). A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data. Environmental Science Nano. 9(1). 189–203. 90 indexed citations

12.

Chatterjee, Mainak & Kunal Roy. (2020). Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors. Journal of Hazardous Materials. 408. 124936–124936. 43 indexed citations

13.

Chatterjee, Mainak, et al.. (2011). AOMDV-APLP. 841–841. 3 indexed citations

14.

Chatterjee, Mainak, et al.. (2010). A Game Theoretic Framework for Power Control in Wireless Sensor Networks (POSTPRINT). 1 indexed citations

15.

Chatterjee, Mainak, et al.. (2010). Incentive and rebuke based reputation model for peer to peer applications. 286–291. 1 indexed citations

16.

De, Swades, Chunming Qiao, Dimitris A. Pados, & Mainak Chatterjee. (2004). Topological and MAI constraints on the performance of wireless CDMA sensor networks. 1. 256–265. 8 indexed citations

17.

Ghosh, Chandradipa, et al.. (2002). Coronary Artery Disease Risk Factors & Their Association with Physical Activity in Older Athletes. European Journal of Cardiovascular Prevention & Rehabilitation. 9(6). 383–392. 3 indexed citations

18.

Chatterjee, Mainak, Sajal K. Das, & Damla Turgut. (2002). WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks. Cluster Computing. 5(2). 193–204. 925 indexed citations breakdown →

19.

Krishna, P. Venkata, Nitin H. Vaidya, Mainak Chatterjee, & Dhiraj Pradhan. (1997). A cluster-based approach for routing in dynamic networks. ACM SIGCOMM Computer Communication Review. 27(2). 49–64. 293 indexed citations

20.

Krishna, P. Venkata, Mainak Chatterjee, Nitin H. Vaidya, & Dhiraj K. Pradhan. (1995). A Cluster-based Approach for Routing in Ad-Hoc Networks. 1–10. 77 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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