Amin Mantrach
- Artificial Intelligence top 5%
- Information Systems top 5%
- Statistical and Nonlinear Physics top 5%
- Computer Vision and Pattern Recognition top 10%
- Management Science and Operations Research top 10%
- Co-authors
- Martin SaveskiMarco SaerensMasashi ShimboNicolas KourtellisLuh YenFabrizio SilvestriAlejandro JaimesCarmen Vaca
- Topics
- Complex Network Analysis Techniques (10 papers)Topic Modeling (5 papers)Web Data Mining and Analysis (5 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligencePattern RecognitionInformation Sciences
- Partner nations
- SpainUnited StatesUnited Kingdom
In The Last Decade
Amin Mantrach
19 papers receiving 515 citations
Peers
Comparison fields: 5 of 69
- Artificial Intelligence 285
- Information Systems 239
- Statistical and Nonlinear Physics 134
- Computer Vision and Pattern Recognition 98
- Management Science and Operations Research 65
Countries citing papers authored by Amin Mantrach
This map shows the geographic impact of Amin Mantrach'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 Amin Mantrach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amin Mantrach more than expected).
Fields of papers citing papers by Amin Mantrach
This network shows the impact of papers produced by Amin Mantrach. 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 Amin Mantrach. The network helps show where Amin Mantrach may publish in the future.
Co-authorship network of co-authors of Amin Mantrach
This figure shows the co-authorship network connecting the top 25 collaborators of Amin Mantrach. A scholar is included among the top collaborators of Amin Mantrach 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 Amin Mantrach. Amin Mantrach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | 7 | |
| 4 | 16 | |
| 5 | 10 | |
| 6 | 9 | |
| 7 | 30 | |
| 8 | 36 | |
| 9 | 38 | |
| 10 | 29 | |
| 11 | 62 | |
| 12 | 8 | |
| 13 | 50 | |
| 14 | 115 | |
| 15 | 6 | |
| 16 | 6 | |
| 17 | 25 | |
| 18 | 35 | |
| 19 | 55 |
About Amin Mantrach
Amin Mantrach is a scholar working on Statistical and Nonlinear Physics, Information Systems and Geometry and Topology, having authored 19 papers that have together received 545 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (10 papers), Topic Modeling (5 papers) and Web Data Mining and Analysis (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (134 citations), Information Systems (239 citations) and Artificial Intelligence (285 citations). Amin Mantrach has collaborated with scholars based in Spain, United States and United Kingdom. Frequent co-authors include Martin Saveski, Marco Saerens, Masashi Shimbo, Nicolas Kourtellis, Luh Yen, Fabrizio Silvestri, Alejandro Jaimes, Carmen Vaca, Luca Maria Aiello and Marina Litvak. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Information Sciences.
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.