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ORCIDCharu C. Aggarwal
Charu Aggarwal 0001
Person information
- affiliation: IBM T. J. Watson Research Center, Yorktown, NY, USA
- affiliation: Indian Institute of Technology Kanpur, Department of Computer Science and Engineering, India
Other persons with the same name
- Charu Aggarwal 0002 — University of Delhi, Department of Computer Science, India
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2020 – today
- 2023
[j106]Falih Gozi Febrinanto, Feng Xia
, Kristen Moore, Chandra Thapa, Charu Aggarwal:
Graph Lifelong Learning: A Survey. IEEE Comput. Intell. Mag. 18(1): 32-51 (2023)- [j105]Feng Xia, Leman Akoglu, Charu Aggarwal, Huan Liu:
Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection. IEEE Intell. Syst. 38(2): 32-35 (2023) - [j104]Jing Ren, Feng Xia, Ivan Lee, Azadeh Noori Hoshyar, Charu C. Aggarwal:
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges. ACM Trans. Intell. Syst. Technol. 14(2): 28:1-28:29 (2023) - [j103]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): 2272-2284 (2023) - [j102]Jianxin Li, Lifang He, Hao Peng, Peng Cui, Charu C. Aggarwal, Philip S. Yu:
Guest Editorial Introduction to the Special Issue on Anomaly Detection in Emerging Data-Driven Applications: Theory, Algorithms, and Applications. IEEE Trans. Knowl. Data Eng. 35(12): 11982-11983 (2023) - [c245]Wenqi Fan, Han Xu, Wei Jin, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, Charu C. Aggarwal:
Jointly Attacking Graph Neural Network and its Explanations. ICDE 2023: 654-667 - [i53]Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu C. Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò:
A Comprehensive Survey of Graph-level Learning. CoRR abs/2301.05860 (2023) - [i52]Zitai Qiu, Jia Wu, Jian Yang, Xing Su, Charu C. Aggarwal:
Heterogeneous Social Event Detection via Hyperbolic Graph Representations. CoRR abs/2302.10362 (2023) - [i51]Zhimeng Guo, Teng Xiao, Charu Aggarwal, Hui Liu, Suhang Wang:
Counterfactual Learning on Graphs: A Survey. CoRR abs/2304.01391 (2023) - [i50]Pengfei He, Han Xu, Jie Ren, Yingqian Cui, Hui Liu, Charu C. Aggarwal, Jiliang Tang:
Sharpness-Aware Data Poisoning Attack. CoRR abs/2305.14851 (2023) - [i49]Haoyu Han, Xiaorui Liu, Feng Shi, MohamadAli Torkamani, Charu C. Aggarwal, Jiliang Tang:
Towards Label Position Bias in Graph Neural Networks. CoRR abs/2305.15822 (2023) - [i48]Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu C. Aggarwal, Sourav Medya:
A Survey on Explainability of Graph Neural Networks. CoRR abs/2306.01958 (2023) - [i47]Zhichao Hou, Xitong Zhang, Wei Wang, Charu C. Aggarwal, Xiaorui Liu:
Can Directed Graph Neural Networks be Adversarially Robust? CoRR abs/2306.02002 (2023) - [i46]Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu C. Aggarwal, Tyler Derr:
Fairness and Diversity in Recommender Systems: A Survey. CoRR abs/2307.04644 (2023) - [i45]Harry Shomer, Yao Ma, Juanhui Li, Bo Wu, Charu C. Aggarwal, Jiliang Tang:
Distance-Based Propagation for Efficient Knowledge Graph Reasoning. CoRR abs/2311.01024 (2023) - 2022
- [b10]Charu C. Aggarwal:
Machine Learning for Text, Second Edition. Springer 2022, ISBN 978-3-030-96622-5, pp. 1-532 - [j101]Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal, Aston Zhang:
Text Style Transfer: A Review and Experimental Evaluation. SIGKDD Explor. 24(1): 14-45 (2022) - [j100]Charu C. Aggarwal:
An Interview with Dr. Charu Aggarwal, Winner of ACM SIGKDD 2022 Service Award. SIGKDD Explor. 24(2): 3-4 (2022) - [j99]Nurendra Choudhary, Charu C. Aggarwal, Karthik Subbian, Chandan K. Reddy:
Self-supervised Short-text Modeling through Auxiliary Context Generation. ACM Trans. Intell. Syst. Technol. 13(3): 51:1-51:21 (2022) - [j98]Charu C. Aggarwal:
Communication from the Editor-in-Chief: State of the ACM Transactions on Knowledge Discovery from Data. ACM Trans. Knowl. Discov. Data 16(2): 21e:1-21e:2 (2022) - [j97]Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe:
Editorial Deep Learning for Anomaly Detection. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2282-2286 (2022) - [c244]Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu C. Aggarwal:
DAGAD: Data Augmentation for Graph Anomaly Detection. ICDM 2022: 259-268 - [c243]Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang:
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective. KDD 2022: 709-719 - [c242]Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng, Leman Akoglu, Charu C. Aggarwal:
Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection. NeurIPS 2022 - [c241]Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal:
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities. SIGIR 2022: 3425-3428 - [c240]Nidhi Rastogi, Sharmishtha Dutta, Alex Gittens, Mohammed J. Zaki, Charu C. Aggarwal:
TINKER: A framework for Open source Cyberthreat Intelligence. TrustCom 2022: 1569-1574 - [c239]Feng Xia, Renaud Lambiotte, Charu Aggarwal:
GraphLearning'22: 1st International Workshop on Graph Learning. WWW (Companion Volume) 2022: 1004-1005 - [i44]Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, Charu Aggarwal:
Graph Lifelong Learning: A Survey. CoRR abs/2202.10688 (2022) - [i43]Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal:
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities. CoRR abs/2205.10759 (2022) - [i42]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 abs/2205.15555 (2022) - [i41]Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang:
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective. CoRR abs/2206.07743 (2022) - [i40]Shijie Zhou, Zhimeng Guo, Charu C. Aggarwal, Xiang Zhang, Suhang Wang:
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning. CoRR abs/2208.01820 (2022) - [i39]Djallel Bouneffouf, Charu C. Aggarwal:
Survey on Applications of Neurosymbolic Artificial Intelligence. CoRR abs/2209.12618 (2022) - [i38]Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu C. Aggarwal:
DAGAD: Data Augmentation for Graph Anomaly Detection. CoRR abs/2210.09766 (2022) - [i37]Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi:
Deep Learning for Time Series Anomaly Detection: A Survey. CoRR abs/2211.05244 (2022) - [i36]Jing Ren, Feng Xia, Azadeh Noori Hoshyar, Charu C. Aggarwal:
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges. CoRR abs/2212.05532 (2022) - 2021
- [b9]Charu C. Aggarwal:
Artificial Intelligence - A Textbook. Springer 2021, ISBN 978-3-030-72356-9, pp. 1-483 - [j96]Liang Duan, Shuai Ma, Charu Aggarwal, Saket Sathe:
Improving spectral clustering with deep embedding, cluster estimation and metric learning. Knowl. Inf. Syst. 63(3): 675-694 (2021) - [j95]Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal:
MetaLearning with Graph Neural Networks: Methods and Applications. SIGKDD Explor. 23(2): 13-22 (2021) - [j94]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): 728-741 (2021) - [j93]Yuxiang Ren, Charu C. Aggarwal, Jiawei Zhang:
ActiveIter: Meta Diagram Based Active Learning in Social Networks Alignment. IEEE Trans. Knowl. Data Eng. 33(5): 1848-1860 (2021) - [c238]Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu C. Aggarwal, Jiliang Tang:
Graph Feature Gating Networks. CIKM 2021: 813-822 - [c237]Enyan Dai, Charu Aggarwal, Suhang Wang:
NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs. KDD 2021: 227-236 - [c236]Guansong Pang, Charu C. Aggarwal:
Toward Explainable Deep Anomaly Detection. KDD 2021: 4056-4057 - [c235]Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal:
Syntax Matters! Syntax-Controlled in Text Style Transfer. RANLP 2021: 566-575 - [c234]Charu C. Aggarwal, Yao Li, Philip S. Yu:
Signature-Based Anomaly Detection in Networks. SDM 2021: 109-117 - [c233]Guansong Pang, Longbing Cao, Charu Aggarwal:
Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities. WSDM 2021: 1127-1130 - [i35]Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu C. Aggarwal:
TINKER: A framework for Open source Cyberthreat Intelligence. CoRR abs/2102.05571 (2021) - [i34]Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal:
Meta-Learning with Graph Neural Networks: Methods and Applications. CoRR abs/2103.00137 (2021) - [i33]Yang Gao, Yi-Fan Li, Yu Lin, Charu C. Aggarwal, Latifur Khan:
SetConv: A New Approach for Learning from Imbalanced Data. CoRR abs/2104.06313 (2021) - [i32]Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu C. Aggarwal, Jiliang Tang:
Graph Feature Gating Networks. CoRR abs/2105.04493 (2021) - [i31]Enyan Dai, Charu C. Aggarwal, Suhang Wang:
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs. CoRR abs/2106.04714 (2021) - [i30]Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu C. Aggarwal, Chang-Tien Lu:
Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks. CoRR abs/2107.10234 (2021) - [i29]Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, Charu C. Aggarwal:
Jointly Attacking Graph Neural Network and its Explanations. CoRR abs/2108.03388 (2021) - [i28]Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal:
Syntax Matters! Syntax-Controlled in Text Style Transfer. CoRR abs/2108.05869 (2021) - [i27]Yu Wang, Charu Aggarwal, Tyler Derr:
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification. CoRR abs/2110.12035 (2021) - 2020
- [b8]Charu C. Aggarwal:
Linear Algebra and Optimization for Machine Learning - A Textbook. Springer 2020, ISBN 978-3-030-40343-0, pp. 1-495 - [j92]Charu Aggarwal:
An Interview with Dr. Charu Aggarwal, SIGKDD Innovation Award Winner. SIGKDD Explor. 22(1): 1-3 (2020) - [j91]Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang:
Adversarial Attacks and Defenses on Graphs. SIGKDD Explor. 22(2): 19-34 (2020) - [c232]Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu C. Aggarwal, Prasenjit Mitra, Suhang Wang:
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. AAAI 2020: 5956-5963 - [c231]Djallel Bouneffouf, Irina Rish, Charu C. Aggarwal:
Survey on Applications of Multi-Armed and Contextual Bandits. CEC 2020: 1-8 - [c230]Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu C. Aggarwal, Prasenjit Mitra, Suhang Wang:
Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. CIKM 2020: 1435-1444 - [c229]Yang Gao, Yi-Fan Li, Yu Lin, Charu C. Aggarwal, Latifur Khan:
SetConv: A New Approach for Learning from Imbalanced Data. EMNLP (1) 2020: 1284-1294 - [c228]Juan-Hui Li, Yao Ma, Yiqi Wang, Charu C. Aggarwal, Chang-Dong Wang, Jiliang Tang:
Graph Pooling with Representativeness. ICDM 2020: 302-311 - [c227]Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray:
Survey on Automated End-to-End Data Science? IJCNN 2020: 1-9 - [c226]Charu C. Aggarwal, Yao Li, Philip S. Yu:
On Supervised Change Detection in Graph Streams. SDM 2020: 289-297 - [c225]Tyler Derr, Yao Ma, Wenqi Fan, Xiaorui Liu, Charu C. Aggarwal, Jiliang Tang:
Epidemic Graph Convolutional Network. WSDM 2020: 160-168 - [i26]Yiqi Wang, Yao Ma, Charu C. Aggarwal, Jiliang Tang:
Non-IID Graph Neural Networks. CoRR abs/2005.12386 (2020) - [i25]Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu C. Aggarwal:
MALOnt: An Ontology for Malware Threat Intelligence. CoRR abs/2006.11446 (2020) - [i24]Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu C. Aggarwal, Prasenjit Mitra, Suhang Wang:
Graph Convolutional Networks against Degree-Related Biases. CoRR abs/2006.15643 (2020) - [i23]Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal:
Text Style Transfer: A Review and Experiment Evaluation. CoRR abs/2010.12742 (2020)
2010 – 2019
- 2019
- [j90]Michele Dallachiesa, Charu C. Aggarwal, Themis Palpanas:
Improving Classification Quality in Uncertain Graphs. ACM J. Data Inf. Qual. 11(1): 3:1-3:20 (2019) - [c224]Bowen Dong, Charu C. Aggarwal, Philip S. Yu:
The Link Regression Problem in Graph Streams. IEEE BigData 2019: 1088-1095 - [c223]Suhang Wang, Charu C. Aggarwal, Huan Liu:
Beyond word2vec: Distance-graph Tensor Factorization for Word and Document Embeddings. CIKM 2019: 1041-1050 - [c222]Kemilly Dearo Garcia, Elaine Ribeiro de Faria, Cláudio Rebelo de Sá, João Mendes-Moreira, Charu C. Aggarwal, André C. P. L. F. de Carvalho, Joost N. Kok:
Ensemble Clustering for Novelty Detection in Data Streams. DS 2019: 460-470 - [c221]Yuxiang Ren, Charu C. Aggarwal, Jiawei Zhang:
Meta Diagram Based Active Social Networks Alignment. ICDE 2019: 1690-1693 - [c220]Liang Duan, Charu C. Aggarwal, Shuai Ma, Saket Sathe:
Improving Spectral Clustering with Deep Embedding and Cluster Estimation. ICDM 2019: 170-179 - [c219]Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi, Charu C. Aggarwal:
Efficient Data Representation by Selecting Prototypes with Importance Weights. ICDM 2019: 260-269 - [c218]Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Bo Zong, Haifeng Chen, Wei Wang:
Self-Attentive Attributed Network Embedding Through Adversarial Learning. ICDM 2019: 758-767 - [c217]Saket Sathe, Charu C. Aggarwal:
Nearest Neighbor Classifiers Versus Random Forests and Support Vector Machines. ICDM 2019: 1300-1305 - [c216]Bowen Dong, Charu C. Aggarwal, Philip S. Yu:
Transfer Learning for Network Classification. IJCNN 2019: 1-8 - [c215]Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal:
Efficient Global String Kernel with Random Features: Beyond Counting Substructures. KDD 2019: 520-528 - [c214]Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang:
Graph Convolutional Networks with EigenPooling. KDD 2019: 723-731 - [c213]Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal:
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. KDD 2019: 1418-1428 - [c212]Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang:
Multi-dimensional Graph Convolutional Networks. SDM 2019: 657-665 - [i22]Yuxiang Ren, Charu C. Aggarwal, Jiawei Zhang:
Meta Diagram based Active Social Networks Alignment. CoRR abs/1902.04220 (2019) - [i21]Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang:
Graph Convolutional Networks with EigenPooling. CoRR abs/1904.13107 (2019) - [i20]Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray:
How can AI Automate End-to-End Data Science? CoRR abs/1910.14436 (2019) - [i19]Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu C. Aggarwal, Prasenjit Mitra, Suhang Wang:
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. CoRR abs/1911.10273 (2019) - [i18]Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal:
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. CoRR abs/1911.11119 (2019) - [i17]Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal:
Efficient Global String Kernel with Random Features: Beyond Counting Substructures. CoRR abs/1911.11121 (2019) - 2018
- [b7]Charu C. Aggarwal:
Neural Networks and Deep Learning - A Textbook. Springer 2018, ISBN 978-3-319-94462-3, pp. 1-497 - [j89]Saket Sathe, Charu C. Aggarwal:
Subspace histograms for outlier detection in linear time. Knowl. Inf. Syst. 56(3): 691-715 (2018) - [j88]Suhang Wang, Charu C. Aggarwal, Huan Liu:
Random-Forest-Inspired Neural Networks. ACM Trans. Intell. Syst. Technol. 9(6): 69:1-69:25 (2018) - [j87]Xindong Wu, Charu C. Aggarwal:
Editorial: TKDD Special Issue on Interactive Data Exploration and Analytics. ACM Trans. Knowl. Discov. Data 12(1): 1:1 (2018) - [c211]Tyler Derr, Charu C. Aggarwal, Jiliang Tang:
Signed Network Modeling Based on Structural Balance Theory. CIKM 2018: 557-566 - [c210]Swarup Chandra, Ahsanul Haque, Hemeng Tao, Jie Liu, Latifur Khan, Charu C. Aggarwal:
Ensemble Direct Density Ratio Estimation for Multistream Classification. ICDE 2018: 1364-1367 - [c209]Pengyang Wang, Yanjie Fu, Jiawei Zhang, Pengfei Wang, Yu Zheng, Charu C. Aggarwal:
You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis. KDD 2018: 2457-2466 - [c208]Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal:
Scalable Spectral Clustering Using Random Binning Features. KDD 2018: 2506-2515 - [c207]Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang:
Learning Deep Network Representations with Adversarially Regularized Autoencoders. KDD 2018: 2663-2671 - [c206]Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, Wei Wang:
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. KDD 2018: 2672-2681 - [c205]Muchen Wu, Charu C. Aggarwal, Zhou Yu, Ananthram Swami, Prasant Mohapatra:
Exploiting Knowledge Across Distinct Domains: Learning Event Details from Network Logs. MILCOM 2018: 1-9 - [c204]Ana Paula Appel, Renato Luiz de Freitas Cunha, Charu C. Aggarwal, Marcela Megumi Terakado:
Temporally Evolving Community Detection and Prediction in Content-Centric Networks. ECML/PKDD (2) 2018: 3-18 - [c203]Pengyang Wang, Jiawei Zhang, Guannan Liu, Yanjie Fu, Charu C. Aggarwal:
Ensemble-Spotting: Ranking Urban Vibrancy via POI Embedding with Multi-view Spatial Graphs. SDM 2018: 351-359 - [c202]Wenchao Yu, Charu C. Aggarwal, Wei Wang:
Modeling Co-Evolution Across Multiple Networks. SDM 2018: 675-683 - [c201]Charu C. Aggarwal:
Extracting Real-Time Insights from Graphs and Social Streams. SIGIR 2018: 1339 - [e16]Xindong Wu, Yew-Soon Ong, Charu C. Aggarwal, Huanhuan Chen:
2018 IEEE International Conference on Big Knowledge, ICBK 2018, Singapore, November 17-18, 2018. IEEE Computer Society 2018, ISBN 978-1-5386-9125-0 [contents] - [r9]Charu C. Aggarwal:
Classification in Streams. Encyclopedia of Database Systems (2nd ed.) 2018 - [r8]Charu C. Aggarwal:
Counterterrorism, Social Network Analysis in. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [r7]Charu C. Aggarwal:
Privacy in Social Networks, Current and Future Research Trends on. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i16]Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal:
Scalable Spectral Clustering Using Random Binning Features. CoRR abs/1805.11048 (2018) - [i15]Ana Paula Appel, Renato Luiz de Freitas Cunha, Charu C. Aggarwal, Marcela Megumi Terakado:
Temporally Evolving Community Detection and Prediction in Content-Centric Networks. CoRR abs/1807.06560 (2018) - [i14]Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang:
Multi-dimensional Graph Convolutional Networks. CoRR abs/1808.06099 (2018) - 2017
- [b6]Charu C. Aggarwal, Saket Sathe:
Outlier Ensembles - An Introduction. Springer 2017, ISBN 978-3-319-54764-0, pp. 1-276 - [j86]Charu C. Aggarwal, Amotz Bar-Noy, Simon Shamoun:
On sensor selection in linked information networks. Comput. Networks 126: 100-113 (2017) - [j85]Karthik Subbian, Charu C. Aggarwal:
Mining Social Streams: Models and Applications. IEEE Data Eng. Bull. 40(3): 100-109 (2017) - [j84]Weike Pan, Qiang Yang, Charu C. Aggarwal, Christoph Koch:
Big Data. IEEE Intell. Syst. 32(2): 7-8 (2017) - [j83]Guo-Jun Qi, Wei Liu, Charu C. Aggarwal, Thomas S. Huang:
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes. IEEE Trans. Pattern Anal. Mach. Intell. 39(7): 1360-1373 (2017) - [j82]Arijit Khan, Charu C. Aggarwal:
Toward query-friendly compression of rapid graph streams. Soc. Netw. Anal. Min. 7(1): 23:1-23:19 (2017) - [j81]