Radhika Garg

687 total citations
32 papers, 452 citations indexed

About

Radhika Garg is a scholar working on Information Systems, Human-Computer Interaction and Artificial Intelligence. According to data from OpenAlex, Radhika Garg has authored 32 papers receiving a total of 452 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Information Systems, 13 papers in Human-Computer Interaction and 12 papers in Artificial Intelligence. Recurrent topics in Radhika Garg's work include Innovative Human-Technology Interaction (13 papers), AI in Service Interactions (10 papers) and Child Development and Digital Technology (8 papers). Radhika Garg is often cited by papers focused on Innovative Human-Technology Interaction (13 papers), AI in Service Interactions (10 papers) and Child Development and Digital Technology (8 papers). Radhika Garg collaborates with scholars based in United States, Switzerland and United Kingdom. Radhika Garg's co-authors include Subhasree Sengupta, Amanda Toler Woodward, Christina Harrington, Burkhard Stiller, Martin Porcheron, Erin Beneteau, Bo Zhang, Leigh Clark, Benjamin R. Cowan and Ying Xu and has published in prestigious journals such as ACM Transactions on Computer-Human Interaction, First Monday and Proceedings of the ACM on Human-Computer Interaction.

In The Last Decade

Radhika Garg

30 papers receiving 438 citations

Peers

Radhika Garg
Comparison fields: 5 of 64
  • Artificial Intelligence 178
  • Human-Computer Interaction 152
  • Sociology and Political Science 109
  • Education 106
  • Social Psychology 84
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Citations per field, relative to Radhika Garg
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Citations per year, relative to Radhika Garg
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Countries citing papers authored by Radhika Garg

Since Specialization
Citations

This map shows the geographic impact of Radhika Garg'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 Radhika Garg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Radhika Garg more than expected).

Fields of papers citing papers by Radhika Garg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Radhika Garg. 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 Radhika Garg. The network helps show where Radhika Garg may publish in the future.

Co-authorship network of co-authors of Radhika Garg

This figure shows the co-authorship network connecting the top 25 collaborators of Radhika Garg. A scholar is included among the top collaborators of Radhika Garg 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 Radhika Garg. Radhika Garg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 1
2 4
3 5
4 28
5 7
6 1
7 31
8 6
9 6
10 9
11 4
12 6
13
Impact of Voice-based Interaction on Learning Practices and Behavior of Children.
4
14
Exploring Everyday Sharing Practices of Smart Speakers.
1
15 38
16 37
17 3
18 7
19 7
20
An Agent Based Approach to Avoid Selfish Node Dynamically in Mobile Networks
1

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.

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