Charų C. Aggarwal

52.0k citations

363 papers · 23.7k indexed · 15 hit papers · h-index 76

Signal Processing top 0.05%
- Data Management and Algorithms 67
- Time Series Analysis and Forecasting 31
Artificial Intelligence top 0.01%
- Advanced Graph Neural Networks 59
- Anomaly Detection Techniques and Applications 55
- Data Stream Mining Techniques 54
Statistical and Nonlinear Physics top 0.1%
- Complex Network Analysis Techniques 69
Information Systems top 0.02%
- Data Mining Algorithms and Applications 49
Computer Networks and Communications top 0.1%
- Advanced Database Systems and Queries 27

Co-authors: Philip S. Yu Joel L. Wolf Jiawei Han Dakshi Agrawal Guo-Jun Qi Thomas S. Huang Karthik Subbian Cecilia M. Procopiuc
Cited by: Signal Processing Artificial Intelligence Statistical and Nonlinear Physics
Journals: Journal of Clinical Oncology (1 paper)SHILAP Revista de lepidopterología (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)
Partner nations: United States China Australia

In The Last Decade

Charų C. Aggarwal

352 papers receiving 22.2k citations

Hit Papers

15 papers align trajectories log scale

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

2024 ACM Computing Surveys
2018 Neural Networks and Deep Learning
2017 Journal of International Crisis and Risk Communication Research
2016 DIAL (Catholic University of Leuven)
2016 Recommender Systems
2015 Data Mining
2015 Journal of International Crisis and Risk Communication Research
2014 Data Classification: Algorithms and Applications
2014 IEEE Transactions on Knowledge and Data Engineering
2013 Outlier Analysis
2013 DATA CLUSTERING
2010 CERN Document Server (European Organization for Nuclear Research)
2001 Outlier detection for high dimensional data
2001 On the design and quantification of privacy preserving data mining algorithms
1999 ACM SIGMOD Record

Peers

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

The 25 scholars most cited alongside Charų C. Aggarwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Charų C. Aggarwal Line = papers co-authored together Charų C. Aggarwal links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series IEEE Transactions on Knowledge and Data Engineering ·Zahra Zamanzadeh Darban,Yiyuan Yang,Geoffrey I. Webb,Charų C. Aggarwal,Qingsong Wen,Shirui Pan,Mahsa Salehi	2025	4
2	Edge Classification on Graphs: New Directions in Topological Imbalance Xueqi Cheng,Yu Wang,Yunchao Liu,Yuying Zhao,Charų C. Aggarwal,Tyler Derr	2025	0
3	Fairness and Diversity in Recommender Systems: A Survey ACM Transactions on Intelligent Systems and Technology ·Yuying Zhao,Yu Wang,Yunchao Liu,Xueqi Cheng,Charų C. Aggarwal,Tyler Derr	2024	16
4	Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation Proceedings of the AAAI Conference on Artificial Intelligence ·Yuying Zhao,Yu Wang,Yi Zhang,Pamela Wiśniewski,Charų C. Aggarwal,Tyler Derr	2024	4
5	State of the Art and Potentialities of Graph-level Learning ACM Computing Surveys ·Zhenyu Yang,Ge Zhang,Jia Wu,Jian Yang,Quan Z. Sheng,Shan Xue,Chuan Zhou,Charų C. Aggarwal,Hao Peng,Wenbin Hu,Edwin R. Hancock,Píetro Lió	2024	13
6	Deep Learning for Time Series Anomaly Detection: A Surveybreakdown → ACM Computing Surveys ·Zahra Zamanzadeh Darban,Geoffrey I. Webb,Shirui Pan,Charų C. Aggarwal,Mahsa Salehi	2024	94
7	Distance-Based Propagation for Efficient Knowledge Graph Reasoning Harry Shomer,Yao Ma,Juanhui Li,Bo Wu,Charų C. Aggarwal,Jiliang Tang	2023	3
8	Neural Networks and Deep Learning Charų C. Aggarwal	2023	54
9	Prevalence of Dental Caries and Dental Fluorosis among 7-12-Year-Old School Children in an Indian Subpopulation: A Cross-Sectional Study SHILAP Revista de lepidopterología ·Charų C. Aggarwal,Meera Sandhu,Vinod Sachdev,Garima Dayal,Namdeo Prabhu,Rakhi Issrani	2021	0
10	Text Style Transfer: A Review and Experiment Evaluation. arXiv (Cornell University) ·Zhiqiang Hu,Roy Ka-Wei Lee,Charų C. Aggarwal	2020	6
11	LODES: Local Density Meets Spectral Outlier Detection Saket Sathe,Charų C. Aggarwal	2016	37
12	Recommender Systems: The Textbook DIAL (Catholic University of Leuven) ·Charų C. Aggarwal	2016	209
13	Query-friendly compression of graph streams Arijit Khan,Charų C. Aggarwal	2016	8
14	Big Graph Privacy EDBT/ICDT Workshops ·Hessam Zakerzadeh,Charų C. Aggarwal,Ken Barker	2015	2
15	Co-author Relationship Prediction in Heterogeneous Bibliographic Networks Yizhou Sun,Rick Barber,Manish Gupta,Charų C. Aggarwal,Jiawei Han	2011	284
16	Privacy-Preserving Data Mining: Models and Algorithms Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) ·Charų C. Aggarwal,Philip S. Yu	2008	297
17	Challenges and experience in prototyping a multi-modal stream analytic and monitoring application on System S Very Large Data Bases ·Kun‐Lung Wu,Kirsten Hildrum,Wei Fan,Philip S. Yu,Charų C. Aggarwal,David George,Buğra Gedik,Eric Bouillet,Xiaohui Gu,Gang Luo,Haixun Wang	2007	61
18	A Framework for Change Diagnosis of Data Streams. International Conference on Management of Data ·Charų C. Aggarwal	2003	11
19	Data Mining Techniques for Personalization. IEEE Data(base) Engineering Bulletin ·Charų C. Aggarwal,Philip S. Yu	2000	17
20	Online generation of profile association rules Knowledge Discovery and Data Mining ·Charų C. Aggarwal,Zheng Sun,Philip S. Yu	1998	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), Advanced Graph Neural Networks (59 papers), Anomaly Detection Techniques and Applications (55 papers), Data Stream Mining Techniques (54 papers), Data Mining Algorithms and Applications (49 papers), Time Series Analysis and Forecasting (31 papers) and Advanced Database Systems and Queries (27 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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