Richi Nayak
- Artificial Intelligence top 2%
- Information Systems top 1%
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Co-authors
- Yuefeng LiYue XuMd Abul BasharThirunavukarasu BalasubramaniamBryan S. LeeXiaohui TaoHuizhi LiangAlsayed Algergawy
- Topics
- Recommender Systems and Techniques (33 papers)Data Mining Algorithms and Applications (26 papers)Data Management and Algorithms (23 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEBioresource Technology
- Partner nations
- AustraliaIndiaUnited States
In The Last Decade
Richi Nayak
168 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 145
- Artificial Intelligence 810
- Information Systems 729
- Computer Networks and Communications 316
- Computer Vision and Pattern Recognition 241
- Signal Processing 235
Countries citing papers authored by Richi Nayak
This map shows the geographic impact of Richi Nayak'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 Richi Nayak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richi Nayak more than expected).
Fields of papers citing papers by Richi Nayak
This network shows the impact of papers produced by Richi Nayak. 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 Richi Nayak. The network helps show where Richi Nayak may publish in the future.
Co-authorship network of co-authors of Richi Nayak
This figure shows the co-authorship network connecting the top 25 collaborators of Richi Nayak. A scholar is included among the top collaborators of Richi Nayak 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 Richi Nayak. Richi Nayak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 16 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 2 | |
| 11 | 8 | |
| 12 | Tensor-based item recommendation using probabilistic ranking in social tagging systems | 14 |
| 13 | Ranking based clustering for social event detection | 7 |
| 14 | 5 | |
| 15 | 25 | |
| 16 | Applications of Data Mining in Healthcare | 1 |
| 17 | 27 | |
| 18 | PCITMiner: prefix-based closed induced tree miner for finding closed induced frequent subtrees | 4 |
| 19 | Efficient Neighbourhood Estimation for Recommenders with Large Datasets | 0 |
| 20 | Data Mining Application in a Software Project Management Process | 1 |
About Richi Nayak
Richi Nayak is a scholar working on Computational Mathematics, Information Systems and Artificial Intelligence, having authored 180 papers that have together received 1.7k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (33 papers), Data Mining Algorithms and Applications (26 papers) and Data Management and Algorithms (23 papers). The work is most often cited by research in Computational Mathematics (40 citations), Information Systems (729 citations) and Artificial Intelligence (810 citations). Richi Nayak has collaborated with scholars based in Australia, India and United States. Frequent co-authors include Yuefeng Li, Yue Xu, Md Abul Bashar, Thirunavukarasu Balasubramaniam, Bryan S. Lee, Xiaohui Tao, Huizhi Liang, Alsayed Algergawy, Gunter Saake and Yuantong Gu. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Bioresource Technology.
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.