Animesh Mukherjee
- Artificial Intelligence top 1%
- Statistical and Nonlinear Physics top 1%
- Information Systems top 5%
- Sociology and Political Science top 10%
- Computer Networks and Communications top 5%
- Co-authors
- Niloy GangulyTanmoy ChakrabortyPawan GoyalMonojit ChoudhuryAyushi DalmiaAnupam BasuPunyajoy SahaBinny Mathew
- Topics
- Complex Network Analysis Techniques (50 papers)Topic Modeling (27 papers)Hate Speech and Cyberbullying Detection (22 papers)
- Partner nations
- IndiaUnited StatesGermany
In The Last Decade
Animesh Mukherjee
146 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 125
- Artificial Intelligence 863
- Statistical and Nonlinear Physics 627
- Information Systems 244
- Sociology and Political Science 200
- Computer Networks and Communications 167
Countries citing papers authored by Animesh Mukherjee
This map shows the geographic impact of Animesh Mukherjee'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 Animesh Mukherjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Animesh Mukherjee more than expected).
Fields of papers citing papers by Animesh Mukherjee
This network shows the impact of papers produced by Animesh Mukherjee. 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 Animesh Mukherjee. The network helps show where Animesh Mukherjee may publish in the future.
Co-authorship network of co-authors of Animesh Mukherjee
This figure shows the co-authorship network connecting the top 25 collaborators of Animesh Mukherjee. A scholar is included among the top collaborators of Animesh Mukherjee 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 Animesh Mukherjee. Animesh Mukherjee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 3 | |
| 13 | 11 | |
| 14 | 5 | |
| 15 | 4 | |
| 16 | 13 | |
| 17 | 4 | |
| 18 | Analysis and Prediction of Question Topic Popularity in Community Q&A Sites: A Case Study of Quora | 11 |
| 19 | 3 | |
| 20 | Unsupervised Parts-of-Speech Induction for Bengali | 2 |
About Animesh Mukherjee
Animesh Mukherjee is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Communication, having authored 158 papers that have together received 1.7k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (50 papers), Topic Modeling (27 papers) and Hate Speech and Cyberbullying Detection (22 papers). The work is most often cited by research in Statistical and Nonlinear Physics (627 citations), Artificial Intelligence (863 citations) and Communication (132 citations). Animesh Mukherjee has collaborated with scholars based in India, United States and Germany. Frequent co-authors include Niloy Ganguly, Tanmoy Chakraborty, Pawan Goyal, Monojit Choudhury, Ayushi Dalmia, Anupam Basu, Punyajoy Saha, Binny Mathew, Sanjukta Bhowmick and Francesca Tria. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.
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.