Palash Goyal
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- Complex Network Analysis Techniques 7
- Artificial Intelligence top 1%
- Advanced Graph Neural Networks 5
- Topic Modeling 3
- Natural Language Processing Techniques 3
- Adversarial Robustness in Machine Learning 2
- Information Systems top 2%
- Transportation top 5%
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- Reservoir Engineering and Simulation Methods 3
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- Multimodal Machine Learning Applications 3
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- Time Series Analysis and Forecasting 2
- Co-authors
- Emilio FerraraSujit Rokka ChhetriArquimedes CanedoSumit PandeyKristina LermanPaulo ShakarianAram GalstyanKai-Wei Chang
- Journals
- Knowledge-Based Systems (2 papers)Journal of Engineering Research (1 paper)Proceedings of the International AAAI Conference on Web and Social Media (1 paper)
- Partner nations
- United StatesAustriaAustralia
In The Last Decade
Palash Goyal
26 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Statistical and Nonlinear Physics 614
- Artificial Intelligence 1.1k
- Information Systems 329
- Computational Mathematics 8
- Transportation 78
Countries citing papers authored by Palash Goyal
This map shows the geographic impact of Palash Goyal'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 Palash Goyal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Palash Goyal more than expected).
Fields of papers citing papers by Palash Goyal
This network shows the impact of papers produced by Palash Goyal. 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 Palash Goyal. The network helps show where Palash Goyal may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Palash Goyal, 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 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 5 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 3 | |
| 10 | 2023 | 20 | |
| 11 | 2022 | 1 | |
| 12 | 2021 | 2 | |
| 13 | 2020 | 3 | |
| 14 | 2020 | 3 | |
| 15 | 2019 | 1 | |
| 16 | dyngraph2vec: Capturing network dynamics using dynamic graph representation learningbreakdown → | 2019 | 278 |
| 17 | 2018 | 56 | |
| 18 | 2018 | 69 | |
| 19 | 2017 | 3 | |
| 20 | 2015 | 4 |
About Palash Goyal
Palash Goyal is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 1.6k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Advanced Graph Neural Networks (5 papers), Topic Modeling (3 papers), Reservoir Engineering and Simulation Methods (3 papers), Multimodal Machine Learning Applications (3 papers), Natural Language Processing Techniques (3 papers), Time Series Analysis and Forecasting (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (614 citations), Artificial Intelligence (1.1k citations) and Information Systems (329 citations). Palash Goyal has collaborated with scholars based in United States, Austria and Australia. Frequent co-authors include Emilio Ferrara, Sujit Rokka Chhetri, Arquimedes Canedo, Sumit Pandey, Kristina Lerman, Paulo Shakarian, Aram Galstyan, Kai-Wei Chang, Rahul Gupta and Richard S. Zemel. Their work appears in journals such as Knowledge-Based Systems, Journal of Engineering Research, Proceedings of the International AAAI Conference on Web and Social Media, SPE Annual Technical Conference and Exhibition and arXiv (Cornell University).
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.