Charu Aggarwal

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 23

Top Topics

Top Venues

IEEE Data Eng. Bull.

IEEE Trans. Knowl. Data Eng.

Publications

Saket Sathe, Charu Aggarwal, Horst Samulowitz, Deepak S. Turaga
Feature-Engineered Random Forests. SDM (2024)
Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks. ACM Comput. Surv. 56 (5) (2024)
Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, Charu Aggarwal
Graph Lifelong Learning: A Survey. IEEE Comput. Intell. Mag. 18 (1) (2023)
Feng Xia, Leman Akoglu, Charu Aggarwal, Huan Liu
Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection. IEEE Intell. Syst. 38 (2) (2023)
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, Sourav Medya
A Survey on Explainability of Graph Neural Networks. IEEE Data Eng. Bull. 46 (2) (2023)
Kunpeng Liu, Yanjie Fu, Le Wu, Xiaolin Li, Charu Aggarwal, Hui Xiong
Automated Feature Selection: A Reinforcement Learning Perspective. IEEE Trans. Knowl. Data Eng. 35 (3) (2023)
Zhimeng Guo, Teng Xiao, Charu Aggarwal, Hui Liu, Suhang Wang
Counterfactual Learning on Graphs: A Survey. CoRR (2023)
Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang
Customized Graph Nerual Networks. IEEE Data Eng. Bull. 46 (2) (2023)
Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, Charu Aggarwal
Graph Lifelong Learning: A Survey. CoRR (2022)
Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe
Editorial Deep Learning for Anomaly Detection. IEEE Trans. Neural Networks Learn. Syst. 33 (6) (2022)
Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities. SIGIR (2022)
Ge Zhang, Jia Wu, Jian Yang, Shan Xue, Wenbin Hu, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu Aggarwal
Graph-level Neural Networks: Current Progress and Future Directions. CoRR (2022)
Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities. CoRR (2022)
Feng Xia, Renaud Lambiotte, Charu Aggarwal
GraphLearning'22: 1st International Workshop on Graph Learning. WWW (Companion Volume) (2022)
Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal
Meta-Learning with Graph Neural Networks: Methods and Applications. CoRR (2021)
Liang Duan, Shuai Ma, Charu Aggarwal, Saket Sathe
Improving spectral clustering with deep embedding, cluster estimation and metric learning. Knowl. Inf. Syst. 63 (3) (2021)
Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal
MetaLearning with Graph Neural Networks: Methods and Applications. SIGKDD Explor. 23 (2) (2021)
Yu Wang, Charu Aggarwal, Tyler Derr
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification. CoRR (2021)
Pengyang Wang, Xiaolin Li, Yu Zheng, Charu Aggarwal, Yanjie Fu
Spatiotemporal Representation Learning for Driving Behavior Analysis: A Joint Perspective of Peer and Temporal Dependencies. IEEE Trans. Knowl. Data Eng. 33 (2) (2021)
Guansong Pang, Longbing Cao, Charu Aggarwal
Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities. WSDM (2021)
Enyan Dai, Charu Aggarwal, Suhang Wang
NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs. KDD (2021)
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang
Adversarial Attacks and Defenses on Graphs. SIGKDD Explor. 22 (2) (2020)
Charu Aggarwal
An Interview with Dr. Charu Aggarwal, SIGKDD Innovation Award Winner. SIGKDD Explor. 22 (1) (2020)