Vikram Goyal
- Artificial Intelligence top 10%
- Information Systems top 5%
- Computational Theory and Mathematics top 10%
- Signal Processing top 10%
- Computer Networks and Communications
- Co-authors
- Tanmoy ChakrabortyDhaval PatelAshish SurekaAnand GuptaMukesh MohaniaShyam GuptaSameep MehtaSushil K. Prasad
- Topics
- Data Mining Algorithms and Applications (9 papers)Topic Modeling (8 papers)Data Management and Algorithms (8 papers)
- Journals
- IEEE Transactions on Knowledge and Data EngineeringLecture notes in computer scienceApplied Intelligence
- Partner nations
- IndiaUnited StatesSingapore
In The Last Decade
Vikram Goyal
40 papers receiving 198 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 124
- Information Systems 123
- Computational Theory and Mathematics 71
- Signal Processing 57
- Computer Networks and Communications 33
Countries citing papers authored by Vikram Goyal
This map shows the geographic impact of Vikram 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 Vikram Goyal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vikram Goyal more than expected).
Fields of papers citing papers by Vikram Goyal
This network shows the impact of papers produced by Vikram 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 Vikram Goyal. The network helps show where Vikram Goyal may publish in the future.
Co-authorship network of co-authors of Vikram Goyal
This figure shows the co-authorship network connecting the top 25 collaborators of Vikram Goyal. A scholar is included among the top collaborators of Vikram Goyal 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 Vikram Goyal. Vikram Goyal 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 | 1 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 3 | |
| 6 | 5 | |
| 7 | 2 | |
| 8 | 6 | |
| 9 | 10 | |
| 10 | Automatic Retrieval of Actionable Information from Disaster-related Microblogs. | 3 |
| 11 | 32 | |
| 12 | Top-K high utility episode mining in complex event sequence | 6 |
| 13 | 20 | |
| 14 | Privacy of location obfuscation | 1 |
| 15 | 1 | |
| 16 | Assistive and Enabling Technology Needs of Elderly People in India: Issues and Initial Results | 6 |
| 17 | 19 | |
| 18 | 2 | |
| 19 | OCHD:Preserving Obliviousness Characteristic of Honeypot Database | 2 |
| 20 | 7 |
About Vikram Goyal
Vikram Goyal is a scholar working on Signal Processing, Computer Science Applications and Artificial Intelligence, having authored 42 papers that have together received 212 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (9 papers), Topic Modeling (8 papers) and Data Management and Algorithms (8 papers). The work is most often cited by research in Information Systems (123 citations), Signal Processing (57 citations) and Computational Theory and Mathematics (71 citations). Vikram Goyal has collaborated with scholars based in India, United States and Singapore. Frequent co-authors include Tanmoy Chakraborty, Dhaval Patel, Ashish Sureka, Anand Gupta, Mukesh Mohania, Shyam Gupta, Sameep Mehta, Sushil K. Prasad, Rishabh Gupta and Raman Kumar. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Lecture notes in computer science and Applied Intelligence.
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.