Megha Khosla
- Artificial Intelligence top 10%
- Molecular Biology
- Atomic and Molecular Physics, and Optics
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Co-authors
- Avishek AnandVinay SettyGraham BrogdenGisa GeroldT. MukaiNikolaos FountoulakisTim ByrnesShinsuke Koyama
- Topics
- Advanced Graph Neural Networks (8 papers)Explainable Artificial Intelligence (XAI) (3 papers)MicroRNA in disease regulation (3 papers)
- Partner nations
- GermanyNetherlandsUnited States
In The Last Decade
Megha Khosla
22 papers receiving 291 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 189
- Molecular Biology 64
- Atomic and Molecular Physics, and Optics 43
- Computer Networks and Communications 28
- Computer Vision and Pattern Recognition 26
Countries citing papers authored by Megha Khosla
This map shows the geographic impact of Megha Khosla'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 Megha Khosla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Megha Khosla more than expected).
Fields of papers citing papers by Megha Khosla
This network shows the impact of papers produced by Megha Khosla. 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 Megha Khosla. The network helps show where Megha Khosla may publish in the future.
Co-authorship network of co-authors of Megha Khosla
This figure shows the co-authorship network connecting the top 25 collaborators of Megha Khosla. A scholar is included among the top collaborators of Megha Khosla 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 Megha Khosla. Megha Khosla 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 | 1 | |
| 3 | 10 | |
| 4 | 7 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 52 | |
| 9 | 4 | |
| 10 | 13 | |
| 11 | 30 | |
| 12 | 9 | |
| 13 | 26 | |
| 14 | Hard Masking for Explaining Graph Neural Networks | 9 |
| 15 | Deep Reinforcement Learning with Graph-based State Representations. | 3 |
| 16 | A Comprehensive Comparison of Unsupervised Network Representation Learning Methods. | 6 |
| 17 | 0 | |
| 18 | 37 | |
| 19 | 44 | |
| 20 | 0 |
About Megha Khosla
Megha Khosla is a scholar working on Artificial Intelligence, Cancer Research and Computer Vision and Pattern Recognition, having authored 25 papers that have together received 298 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Explainable Artificial Intelligence (XAI) (3 papers) and MicroRNA in disease regulation (3 papers). The work is most often cited by research in Health Informatics (11 citations), Artificial Intelligence (189 citations) and Statistical and Nonlinear Physics (25 citations). Megha Khosla has collaborated with scholars based in Germany, Netherlands and United States. Frequent co-authors include Avishek Anand, Vinay Setty, Graham Brogden, Gisa Gerold, T. Mukai, Nikolaos Fountoulakis, Tim Byrnes, Shinsuke Koyama, Κωνσταντίνος Παναγιώτου and Zhenye Wang. Their work appears in journals such as Scientific Reports, BMC Bioinformatics and Cell 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.