Publications

2024 Permalink

J. A. Miller, M. Aldosari, F. Saeed, N. H. Barna, S. Rana, I. B. Arpinar, and N. Liu
A Survey of Deep Learning and Foundation Models for Time Series Forecasting
arXiv preprint arXiv:2401.13912, 2024
arXiv Journal bibtex

@InProceedings{Miller:ACM2024, author = {J. A. Miller, M. Aldosari, F. Saeed, N. H. Barna, S. Rana, I. B. Arpinar, and N. Liu}, title = {A Survey of Deep Learning and Foundation Models for Time Series Forecasting}, booktitle = {arXiv preprint arXiv:2401.13912, 2024}, year = {2024}, }

H. Zhao, H. Chen, F. Yang, N. Liu, H. Deng, H. Cai, S. Wang, D. Yin, and M. Du
Explainability for Large Language Models: A Survey
ACM Transactions on Intelligent Systems and Technology
arXiv Journal bibtex

@InProceedings{Zhao2024Explainability, author = {Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du}, title = {Explainability for large language models: A survey}, booktitle = {ACM Transactions on Intelligent Systems and Technology}, year = {2024}, }

C. Zhao, W. Qian, Y. Shi, M. Huai, and N. Liu
Automated Natural Language Explanation of Deep Visual Neurons with Large Models
arXiv preprint arXiv:2310.10708
arXiv Journal bibtex

@InProceedings{Zhao2024Automated, author = {Chenxu Zhao, Wei Qian, Yucheng Shi, Mengdi Huai, Ninghao Liu}, title = {Automated Natural Language Explanation of Deep Visual Neurons with Large Models}, booktitle = {arXiv preprint arXiv:2310.10708}, year = {2024}, url={https://arxiv.org/abs/2310.10708}, }

X. Wu, H. Zhou, Y. Shi, W. Yao, X. Huang, and N. Liu
Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation
The Web Conference (WWW), 2024
Conference bibtex

@InProceedings{Wu2024Could, author = {X. Wu, H. Zhou, Y. Shi, W. Yao, X. Huang, and N. Liu}, title = {Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation}, booktitle = {The Web Conference (WWW), 2024}, year = {2024}, }

Z. Guan, M. Hu, Z. Zhou, J. Zhang, S. Li, and N. Liu
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks
AAAI, 2024 (student abstract)
arXiv Conference bibtex

@InProceedings{Guan2024BadSam, author = {Z. Guan, M. Hu, Z. Zhou, J. Zhang, S. Li, and N. Liu}, title = {BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks}, booktitle = {AAAI, 2024 (student abstract)}, year = {2024}, url={https://arxiv.org/abs/2305.03289}, }

Y. Wang, K. Zhou, N. Liu, Y. Wang, and X. Wang
Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
ICLR, 2024
OpenReview Conference bibtex

@InProceedings{Wang2024Efficient, author = {Y. Wang, K. Zhou, N. Liu, Y. Wang, and X. Wang}, title = {Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models}, booktitle = {ICLR, 2024}, year = {2024}, url={https://openreview.net/forum?id=Od39h4XQ3Y}, }

2023 Permalink

J. A. Miller, N. H. Barna, S. Rana, I. B. Arpinar, and N. Liu
Knowledge Enhanced Deep Learning: Application to Pandemic Prediction
The 9th IEEE International Conference on Collaboration and Internet Computing, November 1-3, 2023, Atlanta, GA
Talk Conference bibtex
Click for Demo

@InProceedings{Miller2023Knowledge, author = {J. A. Miller, N. H. Barna, S. Rana, I. B. Arpinar, and N. Liu}, title = {Knowledge Enhanced Deep Learning: Application to Pandemic Prediction}, booktitle = {The 9th IEEE International Conference on Collaboration and Internet Computing, November 1-3, 2023, Atlanta, GA}, year = {2023}, url = {https://cobweb.cs.uga.edu/~jam/papers/abs/2023_CIC/IEEECIC_2023_Miller_Talk.pptx}, }

S. Rana, N. H. Barna, and J. A. Miller
Exploring the Predictive Power of Correlation and Mutual Information in Attention Temporal Graph Convolutional Network for COVID-19 Forecasting
Proceedings of the 12th International Conference on Big Data (BigData 2023), Lecture Notes in Computer Science (LNCS, volume 14203), Honolulu, Hawaii (September 23-25, 2023) pp. 18-33
Springer Conference bibtex

@InProceedings{Rana2023Exploring, author = {S. Rana, N. H. Barna, and J. A. Miller}, title = {Exploring the Predictive Power of Correlation and Mutual Information in Attention Temporal Graph Convolutional Network for COVID-19 Forecasting}, booktitle = {Proceedings of the 12th International Conference on Big Data (BigData 2023), Lecture Notes in Computer Science (LNCS, volume 14203), Honolulu, Hawaii (September 23-25, 2023) pp. 18-33}, year = {2023}, url = {https://link.springer.com/chapter/10.1007/978-3-031-44725-9_2}, }

A. Farhadi, D. Chen, R. McCoy, C. Scott, P. Ma, C. M. Vachon, J. Zhang, C. Ngufor, and J. A. Miller
Classification Using Deep Transfer Learning on Structured Healthcare Data
2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1560-1565, Mexico City, Mexico (05-08 December 2023)
IEEE Conference bibtex

@InProceedings{Farhadi2023Classification, author = {A. Farhadi, D. Chen, R. McCoy, C. Scott, P. Ma, C. M. Vachon, J. Zhang, C. Ngufor, and J. A. Miller}, title = {Classification Using Deep Transfer Learning on Structured Healthcare Data}, booktitle = {2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1560-1565, Mexico City, Mexico (05-08 December 2023)}, year = {2023}, url = {https://ieeexplore.ieee.org/abstract/document/10371847}, }

J. A. Miller
Introduction to Computational Data Science Using ScalaTion
School of Computing, University of Georgia, 2023
UGA Online Book bibtex

@Book{Miller2023Introduction, author = {J. A. Miller}, booktitle = {Introduction to Computational Data Science Using ScalaTion}, year = {2023}, url = {https://cobweb.cs.uga.edu/~jam/scalation_guide/comp_data_science.pdf}, }

M. Aldosari, and J. A. Miller
On Transformer Autoregressive Decoding for Multivariate Time Series Forecasting
Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), Bruges, Belgium (October 4-6, 2023) pp. 423-428
ESANN Conference bibtex

@InProceedings{Aldosari2023Transformer, author = {M. Aldosari, and J. A. Miller}, title = {On Transformer Autoregressive Decoding for Multivariate Time Series Forecasting}, booktitle = {Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), Bruges, Belgium (October 4-6, 2023) pp. 423-428}, year = {2023}, url = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-171.pdf}, }

H. Cai, W. Liao, Z. Liu, Y. Zhang, X. Huang, S. Ding, H. Ren, Z. Wu, H. Dai, S. Li, L. Wu, N. Liu, Q. Li, T. Liu, and X. Li
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
International Conference on Bioinformatics and Biomedicine (BIBM)
arXiv Conference bibtex

@InProceedings{Cai2023Coarse, author = {Hongmin Cai, Wenxiong Liao, Zhengliang Liu, Yiyang Zhang, Xiaoke Huang, Siqi Ding, Hui Ren, Zihao Wu, Haixing Dai, Sheng Li, Lingfei Wu, Ninghao Liu, Quanzheng Li, Tianming Liu, Xiang Li}, title = {Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training}, booktitle = {International Conference on Bioinformatics and Biomedicine (BIBM)}, year = {2023}, url = {https://arxiv.org/abs/2211.02849}, }

Y. Shi, M. Du, X. Wu, Z. Guan, and N. Liu
Black-box Backdoor Defense via Zero-shot Image Purification
2023 Conference on Neural Information Processing Systems (NeurIPS 2023)
arXiv Conference bibtex

@InProceedings{Shi2023Black, author = {Yucheng Shi, Mengnan Du, Xuansheng Wu, Zihan Guan, Ninghao Liu}, title = {Black-box Backdoor Defense via Zero-shot Image Purification}, booktitle = {2023 Conference on Neural Information Processing Systems (NeurIPS 2023)}, year = {2023}, url = {https://arxiv.org/abs/2303.12175}, }

Y. Shi, Y. Dong, Q. Tan, J. Li, and N. Liu
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
arXiv Conference bibtex

@InProceedings{Shi2023Gigamae, author = {Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu}, title = {GiGaMAE: Generalizable graph masked autoencoder via collaborative latent space reconstruction}, booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management}, year = {2023}, url = {https://arxiv.org/abs/2308.09663}, }

Z. Guan, M. Du, and N. Liu
XGBD: Explanation-Guided Graph Backdoor Detection
26th European Conference on Artificial Intelligence (ECAI 2023)
arXiv Conference bibtex

@InProceedings{Guan2023XGBD, author = {Zihan Guan, Mengnan Du, Ninghao Liu}, title = {XGBD: Explanation-Guided Graph Backdoor Detection}, booktitle = {26th European Conference on Artificial Intelligence (ECAI 2023)}, year = {2023}, url = {https://arxiv.org/abs/2308.04406}, }

Y. Shi, K. Zhou, and N. Liu
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
arXiv Conference bibtex

@InProceedings{Shi2023Engage, author = {Yucheng Shi, Kaixiong Zhou, Ninghao Liu}, title = {ENGAGE: Explanation guided data augmentation for graph representation learning}, booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases}, year = {2023}, url = {https://arxiv.org/abs/2307.01053}, }

G. Wang, M. Du, N. Liu, N. Zou, and X. Hu
Mitigating Algorithmic Bias with Limited Annotations
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
arXiv Conference bibtex

@InProceedings{Wang2023Mitigating, author = {Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu}, title = {Mitigating algorithmic bias with limited annotations}, booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases}, year = {2023}, url = {https://arxiv.org/abs/2207.10018}, }

X. Wu, X. He, T. Liu, N. Liu, and X. Zhai
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-Shot Prompt Learning for Automatic Scoring in Science Education
International Conference on Artificial Intelligence in Education
Springer Conference bibtex

@InProceedings{Wu2023Matching, author = {Xuansheng Wu, Xinyu He, Tianming Liu, Ninghao Liu, Xiaoming Zhai}, title = {Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-Shot Prompt Learning for Automatic Scoring in Science Education}, booktitle = {International Conference on Artificial Intelligence in Education}, year = {2023}, url = {https://link.springer.com/chapter/10.1007/978-3-031-36272-9_33}, }

K. Zhou, S.-H. Choi, Z. Liu, N. Liu, F. Yang, R. Chen, L. Li, and X. Hu
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)
Springer Conference bibtex

@InProceedings{Zhou2023Adaptive, author = {Kaixiong Zhou, Soo-Hyun Choi, Zirui Liu, Ninghao Liu, Fan Yang, Rui Chen, Li Li, Xia Hu}, title = {Adaptive Label Smoothing To Regularize Large-Scale Graph Training}, booktitle = {Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)}, year = {2023}, url = {https://arxiv.org/abs/2108.13555}, }

Y. Dong, S. Wang, J. Ma, N. Liu, and J. Li
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Proceedings of the AAAI Conference on Artificial Intelligence
ArXiv Conference bibtex

@InProceedings{Dong2023Interpreting, author = {Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li}, title = {Interpreting Unfairness in Graph Neural Networks via Training Node Attribution}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, year = {2023}, url = {https://arxiv.org/abs/2211.14383}, }

L. Hu, Y. Liu, N. Liu, M. Huai, L. Sun, and D. Wang
SEAT: Stable and Explainable Attention
Proceedings of the AAAI Conference on Artificial Intelligence
ArXiv Conference bibtex

@InProceedings{Hu2023SEAT, author = {Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang}, title = {SEAT: Stable and Explainable Attention}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, year = {2023}, url = {https://arxiv.org/abs/2211.13290}, }

Q. Tan, N. Liu, X. Huang, S.-H. Choi, L. Li, R. Chen, X. Hu
S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
ACM Conference bibtex

@InProceedings{Tan2023S2GAE, author = {Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, Xia Hu}, title = {S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking}, booktitle = {Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining}, year = {2023}, url = {https://dl.acm.org/doi/abs/10.1145/3539597.3570404}, }

Q. Tan, X. Zhang, N. Liu, D. Zha, L. Li, R. Chen, S.-H. Choi, and X. Hu
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
arXiv Conference bibtex

@InProceedings{Tan2023Bring, author = {Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu}, title = {Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection}, booktitle = {Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining}, year = {2023}, url = {https://arxiv.org/abs/2212.12488}, }

Q. Tan, D. Zha, N. Liu, S.-H. Choi, L. Li, R. Chen, and X. Hu
Double Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation
IEEE International Conference on Data Mining (ICDM), 2023
openReview Conference bibtex

@InProceedings{Tan2023Double, author = {Q. Tan, D. Zha, N. Liu, S.-H. Choi, L. Li, R. Chen, and X. Hu}, title = {Double Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation}, booktitle = {IEEE International Conference on Data Mining (ICDM), 2023}, year = {2023}, url = {https://openreview.net/forum?id=NGIFt6BNvLe}, }

Z. Guan, L. Sun, M. Du, and N.Liu
Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks
The Conference on Information and Knowledge Management (CIKM), 2023
ACM Conference bibtex

@InProceedings{Guan2023Attacking, author = {Z. Guan, L. Sun, M. Du, and N.Liu}, title = {Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks}, booktitle = {The Conference on Information and Knowledge Management (CIKM), 2023}, year = {2023}, url = {https://dl.acm.org/doi/pdf/10.1145/3583780.3614784}, }

G. Wang, Z. Liu, Z. Jiang, N. Liu, N. Zou, and X. Hu
DIVISION: Memory Efficient Training via Dual Activation Precision
International Conference on Machine Learning (ICML), 2023
arXiv Conference bibtex

@InProceedings{Wang2023Division, author = {G. Wang, Z. Liu, Z. Jiang, N. Liu, N. Zou, and X. Hu}, title = {DIVISION: Memory Efficient Training via Dual Activation Precision}, booktitle = {International Conference on Machine Learning (ICML), 2023}, year = {2023}, url = {https://arxiv.org/abs/2208.04187}, }

S. Zhou, X. Huang, N. Liu, F.-L. Chung, and L.-K. Huang
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023
arXiv Journal bibtex

@InProceedings{Zhou2023Improving, author = {S. Zhou, X. Huang, N. Liu, F.-L. Chung, and L.-K. Huang}, title = {Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation}, booktitle = {IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023}, year = {2023}, url = {https://arxiv.org/abs/2209.10168}, }

R. Tang, Q. Feng, N. Liu, F. Yang, and X. Hu
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
SIGKDD Exploration Newsletter, 2023
arXiv Journal bibtex

@InProceedings{Thang2023Did, author = {R. Tang, Q. Feng, N. Liu, F. Yang, and X. Hu}, title = {Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking}, booktitle = {SIGKDD Exploration Newsletter, 2023}, year = {2023}, url = {https://arxiv.org/abs/2303.11470}, }

X. Wu, K. Zhou, M. Sun, X. Wang, and N. Liu
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
arXiv Preprint, 2023
arXiv Article bibtex

@InProceedings{Wu2023Survey, author = {X. Wu, K. Zhou, M. Sun, X. Wang, and N. Liu}, title = {A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges}, booktitle = {arXiv Preprint, 2023}, year = {2023}, url = {https://arxiv.org/abs/2303.07275}, }

2022 Permalink

C. Bowman, J. A. Miller, and Y. Wang
Microscopic Vehicular Traffic Simulation: Toward Online Calibration
Proceedings of the 2022 IEEE/ACM Winter Simulation Conference (WSC 2022), Singapore (December 11-14, 2022) pp. 2234-2245
IEEE Conference bibtex

@InProceedings{Bowman022Microscopic, author = {C. Bowman, J. A. Miller, and Y. Wang}, title = {Microscopic Vehicular Traffic Simulation: Toward Online Calibration}, booktitle = {Proceedings of the 2022 IEEE/ACM Winter Simulation Conference (WSC 2022), Singapore (December 11-14, 2022) pp. 2234-2245}, year = {2022}, url = {https://ieeexplore.ieee.org/abstract/document/10015345}, }

M. Iman, J. A. Miller, K. Rasheed, R. M. Branch, and H. R. Arabnia
EXPANSE: A Continual and Progressive Learning System for Deep Transfer Learning
Proceedings of the 2022 International Conference on Computational Science and Computational Intelligence (CSCI 2022) Las Vegas, Nevada (December 14-16, 2022) pp. 58-65
arXiv Conference bibtex

@InProceedings{Iman2022Expanse, author = {M. Iman, J. A. Miller, K. Rasheed, R. M. Branch, and H. R. Arabnia}, title = {Microscopic Vehicular Traffic Simulation: Toward Online Calibration}, booktitle = {Proceedings of the 2022 International Conference on Computational Science and Computational Intelligence (CSCI 2022) Las Vegas, Nevada (December 14-16, 2022) pp. 58-65}, year = {2022}, url = {https://arxiv.org/abs/2205.10356}, }

J. A. Miller, and R. Mahmud
Research Directions in Process Modeling and Mining Using Knowledge Graphs and Machine Learning
Proceedings of the 19th International Conference on Services Computing (SCC 2022), Lecture Notes in Computer Science (LNCS, volume 13738), Honolulu, Hawaii (December 10-14, 2022) pp. 86-100
Springer Conference bibtex

@InProceedings{Miller2022Research, author = {J. A. Miller, and R. Mahmud}, title = {Research Directions in Process Modeling and Mining Using Knowledge Graphs and Machine Learning}, booktitle = {Proceedings of the 19th International Conference on Services Computing (SCC 2022), Lecture Notes in Computer Science (LNCS, volume 13738), Honolulu, Hawaii (December 10-14, 2022) pp. 86-100}, year = {2022}, url = {https://link.springer.com/chapter/10.1007/978-3-031-23515-3_7}, }

X. Han, Z. Jiang, N. Liu, and X. Hu
G-Mixup: Graph Data Augmentation for Graph Classification
International Conference on Machine Learning (ICML), 2022 - awarded an Outstanding Paper Award at ICML 2022.
arXiv Conference bibtex

@InProceedings{ author = {Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu}, booktitle = {International Conference on Machine Learning (ICML), 2022}, title = {G-Mixup: Graph Data Augmentation for Graph Classification}, year = {2022}, url={https://arxiv.org/abs/2202.07179}, }

Y. Wang, K. Zhou, R. Miao, N. Liu, and X. Wang
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
Proceedings of the 31st ACM International Conference on Information & Knowledge Management
ACM Conference bibtex

@InProceedings{Wang2022AdaGCL, author = {Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang}, title = {AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training}, booktitle = {Proceedings of the 31st ACM International Conference on Information & Knowledge Management}, year = {2022}, url = {https://dl.acm.org/doi/10.1145/3511808.3557228}, }

Y. Dong, N. Liu, B. Jalaian, and J. Li
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Proceedings of the ACM Web Conference, 2022
arXiv Conference bibtex

@InProceedings{Dong2022Processings, author = {Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li}, title = {EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks}, booktitle = {Proceedings of the ACM Web Conference, 2022}, year = {2022}, url = {https://arxiv.org/abs/2108.05233}, }

Z. Yang, N. Liu, X. B. Hu, and F. Jin
Tutorial on Deep Learning Interpretation: A Data Perspective
Proceedings of the 31st ACM International Conference on Information & Knowledge Management
ACM Conference bibtex

@InProceedings{Yang2022Tutorial, author = {Zhou Yang, Ninghao Liu, Xia Ben Hu, Fang Jin}, title = {EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks}, booktitle = {Proceedings of the 31st ACM International Conference on Information & Knowledge Management}, year = {2022}, url = {https://dl.acm.org/doi/abs/10.1145/3511808.3557500}, }

W. Song, Y. Dong, N. Liu, and J. Li
GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
ACM Conference bibtex

@InProceedings{Song2022Guide, author = {Weihao Song, Yushun Dong, Ninghao Liu, Jundong Li}, title = {GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks}, booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, year = {2022}, url = {https://dl.acm.org/doi/abs/10.1145/3534678.3539346}, }

Q. Feng, N. Liu, F. Yang, R. Tang, M. Du, and X. Hu
DEGREE: Decomposition Based Explanation For Graph Neural Networks
International Conference on Learning Representations (ICLR)
arXiv Conference bibtex

@InProceedings{Feng2022Degree, author = {Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu}, title = {DEGREE: Decomposition Based Explanation For Graph Neural Networks}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2022}, url = {hhttps://arxiv.org/abs/2305.12895}, }

X. Han, Z. Jiang, N. Liu, Q. Song, J. Li, and X. Hu
Geometric Graph Representation Learning via Maximizing Rate Reduction
International Conference on Learning Representations (ICLR)
ACM Conference bibtex

@InProceedings{Han2022Geometric, author = {Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu}, title = {Geometric Graph Representation Learning via Maximizing Rate Reduction}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2022}, url = {https://dl.acm.org/doi/pdf/10.1145/3485447.3512170}, }

N. Liu, Q. Feng, and X. Hu
Interpretability in Graph Neural Networks
Graph Neural Networks: Foundations, Frontiers, and Applications
Github Book Chapter bibtex

@InProceedings{Liu2022Interpretability, author = {Ninghao Liu, Qizhang Feng, Xia Hu}, title = {Interpretability in Graph Neural Networks}, booktitle = {Graph Neural Networks: Foundations, Frontiers, and Applications}, year = {2022}, url = {https://graph-neural-networks.github.io/static/file/chapter7.pdf}, }

S. Zhou, X. Huang, N. Liu, Q. Tan, F.-L. Chung
Unseen Anomaly Detection on Networks via Multi-hypersphere Learning
Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)
Siam Conference bibtex

@InProceedings{Zhou2022Unseen, author = {Shuang Zhou, Xiao Huang, Ninghao Liu, Qiaoyu Tan, Fu-Lai Chung}, title = {Unseen Anomaly Detection on Networks via Multi-hypersphere Learning}, booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)}, year = {2022}, url = {https://epubs.siam.org/doi/pdf/10.1137/1.9781611977172.30}, }

M. Wan, D. Zha, N. Liu, and N. Zou
Modeling Techniques for Machine Learning Fairness: A Survey
Transactions on Knowledge Discovery from Data (TKDD), 2022
arXiv Journal bibtex

@InProceedings{Wan2022Modelling, author = {M. Wan, D. Zha, N. Liu, and N. Zou}, title = {Modeling Techniques for Machine Learning Fairness: A Survey}, booktitle = {Transactions on Knowledge Discovery from Data (TKDD), 2022}, year = {2022}, url = {https://arxiv.org/abs/2111.03015}, }

2021 Permalink

M. Toutiaee, X. Li, Y. Chaudhari, S. Sivaraja, A. Venkataraj, I. Javeri, Y. Ke, I. B. Arpinar, N. Lazar, and J. A. Miller
Improving COVID-19 Forecasting using Exogenous Variables
Proceedings of the 7th ACM KDD Workshop on Mining and Learning from Time Series (MileTS 2021) Virtual/Singapore (August 2021) pp. 1-6
arXiv Conference bibtex

@InProceedings{Toutee2023Improving, author = {M. Toutiaee, X. Li, Y. Chaudhari, S. Sivaraja, A. Venkataraj, I. Javeri, Y. Ke, I. B. Arpinar, N. Lazar, and J. A. Miller}, title = {Improving COVID-19 Forecasting using Exogenous Variables}, booktitle = {Proceedings of the 7th ACM KDD Workshop on Mining and Learning from Time Series (MileTS 2021) Virtual/Singapore (August 2021) pp. 1-6}, year = {2021}, url = {https://https://arxiv.org/abs/2107.10397}, }

Y. Chaudhari, I. Javeri, I. B. Arpinar, J. A. Miller, X. Li, B. Li, Y. Ke, M. Toutiaee, and N. Lazar
Enhance COVID-19 Mortality Prediction with Human Mobility Trend and Medical Information
Proceedings of the 7th IEEE International Conference on Data Science and Systems DSS 2021), Virtual/Haikou, Hainan, China, (December 2021) pp. 1-8
IEEE Conference bibtex

@InProceedings{Chaudhari2021Enhance author = {Y. Chaudhari, I. Javeri, I. B. Arpinar, J. A. Miller, X. Li, B. Li, Y. Ke, M. Toutiaee, and N. Lazar}, title = {Enhance COVID-19 Mortality Prediction with Human Mobility Trend and Medical Information}, booktitle = {Proceedings of the 7th IEEE International Conference on Data Science and Systems DSS 2021), Virtual/Haikou, Hainan, China, (December 2021) pp. 1-8}, year = {2021}, url = {https://ieeexplore.ieee.org/document/9780972}, }

I. Javeri, M. Toutiaee, I. B. Arpinar, T. Miller, and J. Miller
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML
Proceedings of the 7th IEEE International Conference on Big Data Computing Service and Machine Learning Application (BigDataService 2021), Virtual/Oxford, United Kingdom (August 2021) pp. 1-8
IEEE Conference bibtex

@InProceedings{Javeri2021Improving author = {I. Javeri, M. Toutiaee, I. B. Arpinar, T. Miller, and J. Miller}, title = {Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML}, booktitle = {Proceedings of the 7th IEEE International Conference on Big Data Computing Service and Machine Learning Application (BigDataService 2021), Virtual/Oxford, United Kingdom (August 2021) pp. 1-8}, year = {2021}, url = {https://ieeexplore.ieee.org/document/9564380}, }

N. Liu, M. Du, R. Guo, H. Liu, and X. Hu
Adversarial Attacks and Defenses: An Interpretation Perspective
ACM SIGKDD Explorations Newsletter
arXiv Journal bibtex

@InProceedings{Liu2021Adversarial author = {Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu}, title = {Adversarial Attacks and Defenses: An Interpretation Perspective}, booktitle = {ACM SIGKDD Explorations Newsletter}, year = {2021}, url = {https://arxiv.org/abs/2004.11488}, }

Q. Tan, J. Zhang, N. Liu, X. Huang, H. Yang, J. Zhou, and X. Hu
Dynamic Memory based Attention Network for Sequential Recommendation
Proceedings of the AAAI Conference on Artificial Intelligence
arXiv Conference bibtex

@InProceedings{Tan2021Dynamic author = {Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu}, title = {Dynamic Memory based Attention Network for Sequential Recommendation}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, year = {2021}, url = {https://arxiv.org/abs/2102.09269}, }

F. Yang, N. Liu, M. Du, and X. Hu
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
SIGKDD Exploration Newsletter, 2021
arXiv Journal bibtex

@InProceedings{Yang2021Generative author = {F. Yang, N. Liu, M. Du, and X. Hu}, title = {Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation}, booktitle = {SIGKDD Exploration Newsletter, 2021}, year = {2021}, url = {https://arxiv.org/abs/2101.06930}, }

M. Du, N. Liu, F. Yang, and X. Hu
Learning Credible DNNs via Incorporating Prior Knowledge and Model Local Explanation
Knowledge and Information Systems (KAIS), 2021
arXiv Conference bibtex

@InProceedings{Du2021Learning author = {M. Du, N. Liu, F. Yang, and X. Hu}, title = {Learning Credible DNNs via Incorporating Prior Knowledge and Model Local Explanation}, booktitle = {Knowledge and Information Systems (KAIS), 2021}, year = {2021}, url = {https://arxiv.org/abs/1908.05601}, }

Q. Tan, J. Zhang, N. Liu, X. Huang, H. Yang, J. Zhou, and X. Hu
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
arXiv Preprint, 2021
arXiv Preprint bibtex

@InProceedings{Tan2021Exad author = {Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu}, title = {ExAD: An Ensemble Approach for Explanation-based Adversarial Detection}, booktitle = {arXiv Preprint, 2021}, year = {2021}, url = {https://arxiv.org/abs/2103.11526}, }

W. Fu, M. Wang, M. Du, N. Liu, S. Hao, and X. Hu
Differentiated Explanation of Deep Neural Networks with Skewed Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
IEEE Conference bibtex

@InProceedings{Fu2021Differentiated author = {Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao, Xia Hu}, title = {Differentiated Explanation of Deep Neural Networks with Skewed Distributions}, booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021}, year = {2021}, url = {https://ieeexplore.ieee.org/document/9316988}, }

Q. Tan, J. Zhang, J. Yao, N. Liu, J. Zhou, H. Yang, and X. Hu
Sparse-Interest Network for Sequential Recommendation
WSDM'21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, March 2021, Pages 598–606
arXiv Conference bibtex

@InProceedings{Tan2021Sparse author = {Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu}, title = {Sparse-Interest Network for Sequential Recommendation}, booktitle = {WSDM'21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, March 2021, Pages 598–606}, year = {2021}, url = {https://arxiv.org/abs/2102.09267}, }