Mohamed Nadif
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Statistical and Nonlinear Physics top 5%
- Molecular Biology
- Information Systems top 5%
- Co-authors
- Gérard GovaertLazhar LabiodAghiles SalahG. GovaertBlaise HanczarFlorence DémenaisSimon FossierFrancis Guillemin
- Topics
- Advanced Clustering Algorithms Research (42 papers)Bayesian Methods and Mixture Models (27 papers)Complex Network Analysis Techniques (16 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceEuropean Journal of Operational ResearchPattern Recognition
- Partner nations
- FranceBelgiumUnited Kingdom
In The Last Decade
Mohamed Nadif
81 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 120
- Artificial Intelligence 903
- Computer Vision and Pattern Recognition 329
- Statistical and Nonlinear Physics 233
- Molecular Biology 190
- Information Systems 185
Countries citing papers authored by Mohamed Nadif
This map shows the geographic impact of Mohamed Nadif'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 Mohamed Nadif with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohamed Nadif more than expected).
Fields of papers citing papers by Mohamed Nadif
This network shows the impact of papers produced by Mohamed Nadif. 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 Mohamed Nadif. The network helps show where Mohamed Nadif may publish in the future.
Co-authorship network of co-authors of Mohamed Nadif
This figure shows the co-authorship network connecting the top 25 collaborators of Mohamed Nadif. A scholar is included among the top collaborators of Mohamed Nadif 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 Mohamed Nadif. Mohamed Nadif 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 | 2 | |
| 3 | 13 | |
| 4 | 3 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 11 | |
| 8 | 19 | |
| 9 | 7 | |
| 10 | 50 | |
| 11 | 15 | |
| 12 | 10 | |
| 13 | 2 | |
| 14 | Precision-recall space to correct external indices for biclustering | 4 |
| 15 | 18 | |
| 16 | Classification de données ordinales : modèles et algorithmes | 2 |
| 17 | Algorithms for Model-based Block Gaussian Clustering. | 4 |
| 18 | 10 | |
| 19 | 8 | |
| 20 | Classification de données qualitatives et modèles | 1 |
About Mohamed Nadif
Mohamed Nadif is a scholar working on Computational Mathematics, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 84 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (42 papers), Bayesian Methods and Mixture Models (27 papers) and Complex Network Analysis Techniques (16 papers). The work is most often cited by research in Computational Mathematics (65 citations), Artificial Intelligence (903 citations) and Statistical and Nonlinear Physics (233 citations). Mohamed Nadif has collaborated with scholars based in France, Belgium and United Kingdom. Frequent co-authors include Gérard Govaert, Lazhar Labiod, Aghiles Salah, G. Govaert, Blaise Hanczar, Florence Démenais, Simon Fossier, Francis Guillemin, Orianne Dumas and Régis Matran. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, European Journal of Operational Research and Pattern Recognition.
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.