Ines Chami
Impact in
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- Data Quality and Management
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- Topic Modeling
- Advanced Graph Neural Networks
- Natural Language Processing Techniques
- Semantic Web and Ontologies
Papers in ⓘ
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- Advanced Image and Video Retrieval Techniques 2
- Multimodal Machine Learning Applications 2
- Image Retrieval and Classification Techniques 2
- Advanced Vision and Imaging 1
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- Advanced Graph Neural Networks 2
- Privacy-Preserving Technologies in Data 1
- Co-authors
- Laurel Orr (1 shared paper)Christopher Ré (2 shared papers)Avanika Narayan (1 shared paper)Rex Ying (1 shared paper)Cristina Re (1 shared paper)Jure Leskovec (1 shared paper)Hervé Le Borgne (2 shared papers)Adrian Popescu (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (1 paper)arXiv (Cornell University) (2 papers)PubMed (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (2 papers)
- Partner nations
- United StatesFrance
In The Last Decade
Ines Chami
5 papers receiving 110 citations
Peers
Comparison fields: 5 of 38
- Management Science and Operations Research 31
- Artificial Intelligence 72
- Computational Mathematics 1
- Health Informatics 2
- Information Systems and Management 7
Countries citing papers authored by Ines Chami
This map shows the geographic impact of Ines Chami'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 Ines Chami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ines Chami more than expected).
Fields of papers citing papers by Ines Chami
This network shows the impact of papers produced by Ines Chami. 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 Ines Chami. The network helps show where Ines Chami may publish in the future.
Co-authors
The 11 scholars most cited alongside Ines Chami, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 72 | |
| 2 | Hyperbolic Graph Convolutional Neural Networks. | 2019 | 34 |
| 3 | 2017 | 3 | |
| 4 | From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. | 2020 | 1 |
| 5 | Image Annotation and Two Paths to Text Illustration. | 2016 | 1 |
| 6 | 2021 | 0 |
About Ines Chami
Ines Chami is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistical and Nonlinear Physics, Management Science and Operations Research and Molecular Biology, having authored 6 papers that have together received 111 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Multimodal Machine Learning Applications (2 papers), Image Retrieval and Classification Techniques (2 papers), Complex Network Analysis Techniques (1 paper), Bioinformatics and Genomic Networks (1 paper), Privacy-Preserving Technologies in Data (1 paper) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Management Science and Operations Research (31 citations), Artificial Intelligence (72 citations), Computational Mathematics (1 citation), Health Informatics (2 citations) and Information Systems and Management (7 citations). Ines Chami has collaborated with scholars based in United States and France. Frequent co-authors include Laurel Orr, Christopher Ré, Avanika Narayan, Rex Ying, Cristina Re, Jure Leskovec, Hervé Le Borgne, Adrian Popescu, Albert Gu and Dat Nguyen. Their work appears in journals such as Proceedings of the VLDB Endowment, arXiv (Cornell University), PubMed and HAL (Le Centre pour la Communication Scientifique Directe).
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.