Shivakumar Vaithyanathan
About
In The Last Decade
Shivakumar Vaithyanathan
44 papers receiving 5.5k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 5.2k
- Information Systems 1.8k
- Sociology and Political Science 528
- Management Science and Operations Research 528
- Computer Networks and Communications 462
Countries citing papers authored by Shivakumar Vaithyanathan
This map shows the geographic impact of Shivakumar Vaithyanathan'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 Shivakumar Vaithyanathan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shivakumar Vaithyanathan more than expected).
Fields of papers citing papers by Shivakumar Vaithyanathan
This network shows the impact of papers produced by Shivakumar Vaithyanathan. 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 Shivakumar Vaithyanathan. The network helps show where Shivakumar Vaithyanathan may publish in the future.
Co-authorship network of co-authors of Shivakumar Vaithyanathan
This figure shows the co-authorship network connecting the top 25 collaborators of Shivakumar Vaithyanathan. A scholar is included among the top collaborators of Shivakumar Vaithyanathan 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 Shivakumar Vaithyanathan. Shivakumar Vaithyanathan 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 | Proceedings of the International Workshop on Data Science for Macro-Modeling | 1 |
| 3 | 8 | |
| 4 | Extracting, Linking and Integrating Data from Public Sources: A Financial Case Study. | 37 |
| 5 | The Power of Declarative Languages: From Information Extraction to Machine Learning. | 1 |
| 6 | 5 | |
| 7 | Domain Adaptation of Rule-Based Annotators for Named-Entity Recognition Tasks | 87 |
| 8 | SystemT: An Algebraic Approach to Declarative Information Extraction | 100 |
| 9 | 8 | |
| 10 | 13 | |
| 11 | 109 | |
| 12 | OLAP over imprecise data with domain constraints | 23 |
| 13 | 115 | |
| 14 | Generalized Opinion Pooling. | 15 |
| 15 | 70 | |
| 16 | An exploration of sentiment summarization | 34 |
| 17 | Thumbs up? Sentiment Classiflcation using Machine Learning Techniques | 29 |
| 18 | Hierarchical Unsupervised Learning | 6 |
| 19 | Generalized Model Selection for Unsupervised Learning in High Dimensions | 38 |
| 20 | Model Selection in Unsupervised Learning with Applications To Document Clustering | 36 |
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.