Charų C. Aggarwal

52.0k citations
363 papers · 23.7k indexed · 15 hit papers · h-index 76
Topics
Complex Network Analysis Techniques (69 papers)Data Management and Algorithms (67 papers)Advanced Graph Neural Networks (59 papers)

In The Last Decade

Charų C. Aggarwal

352 papers receiving 22.2k citations

Hit Papers

Neural Networks and Deep...19992026200820172018200120142014199950010001.5k

Peers

Charų C. Aggarwal
Comparison fields: 5 of 217
  • Artificial Intelligence 14.3k
  • Information Systems 5.8k
  • Computer Networks and Communications 4.9k
  • Signal Processing 4.8k
  • Computer Vision and Pattern Recognition 3.7k
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Countries citing papers authored by Charų C. Aggarwal

Since Specialization
Citations

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

Fields of papers citing papers by Charų C. Aggarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charų C. Aggarwal. 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 Charų C. Aggarwal. The network helps show where Charų C. Aggarwal may publish in the future.

Co-authorship network of co-authors of Charų C. Aggarwal

This figure shows the co-authorship network connecting the top 25 collaborators of Charų C. Aggarwal. A scholar is included among the top collaborators of Charų C. Aggarwal 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 Charų C. Aggarwal. Charų C. Aggarwal 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
#WorkIndexed citations
1 4
2 0
3 16
4 4
5 13
6
Deep Learning for Time Series Anomaly Detection: A Surveybreakdown →
94
7 3
8 54
9
Prevalence of Dental Caries and Dental Fluorosis among 7-12-Year-Old School Children in an Indian Subpopulation: A Cross-Sectional Study
0
10
Text Style Transfer: A Review and Experiment Evaluation.
6
11 37
12 209
13 8
14
Big Graph Privacy
2
15 284
16 297
17
Challenges and experience in prototyping a multi-modal stream analytic and monitoring application on System S
61
18
A Framework for Change Diagnosis of Data Streams.
11
19
Data Mining Techniques for Personalization.
17
20
Online generation of profile association rules
10

About Charų C. Aggarwal

Charų C. Aggarwal is a scholar working on Signal Processing, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 363 papers that have together received 23.7k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (69 papers), Data Management and Algorithms (67 papers) and Advanced Graph Neural Networks (59 papers). The work is most often cited by research in Signal Processing (4.8k citations), Artificial Intelligence (14.3k citations) and Statistical and Nonlinear Physics (3.3k citations). Charų C. Aggarwal has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Philip S. Yu, Joel L. Wolf, Jiawei Han, Dakshi Agrawal, Guo-Jun Qi, Thomas S. Huang, Karthik Subbian, Cecilia M. Procopiuc, Saket Sathe and Haixun Wang. Their work appears in journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine 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.

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