Publications

2023

• Adaptive Label Smoothing To Regularize Large-Scale Graph Training
  Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
  SIAM International Conference on Data Mining (SDM), 2023
  

• Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
  Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li
  AAAI Conference on Artificial Intelligence (AAAI), 2023
  

• SEAT: Stable and Explainable Attention
  Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
  AAAI Conference on Artificial Intelligence (AAAI), 2023
  

• S2GAE: Self-Supervised Graph Autoencoders Are Generalizable Learners with Graph Masking
  Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, Xia Hu
  WSDM, 2023

• Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
  Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu
  WSDM, 2023

2022

• Modeling Techniques for Machine Learning Fairness: A Survey
  Mingyang Wan, Daochen Zha, Ninghao Liu, Na Zou
  Transactions on Knowledge Discovery from Data (TKDD), 2022
  

• AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
  Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang
  International Conference on Information and Knowledge Management (CIKM), 2022
  [slides]
  

• Tutorial on Deep Learning Interpretation: A Data Perspective
  Zhou Yang, Ninghao Liu, Xia Ben Hu, Fang Jin
  International Conference on Information & Knowledge Management (CIKM), 2022
  

• GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
  Weihao Song, Yushun Dong, Ninghao Liu, Jundong Li
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
  

• G-Mixup: Graph Data Augmentation for Graph Classification
  Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
  International Conference on Machine Learning (ICML), 2022, [Code]
  Outstanding Paper Award
  

• DEGREE: Decomposition Based Explanation for Graph Neural Networks
  Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu
  ICLR, 2022, [Code]
  

• EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
  Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li
  The Web Conference (WWW), 2022
  

• Geometric Graph Representation Learning via Maximizing Rate Reduction
  Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu
  The Web Conference (WWW), 2022, [Code]
  

• Interpretability in Graph Neural Networks
  Ninghao Liu, Qizhang Feng, Xia Hu
  Graph Neural Networks: Foundations, Frontiers, and Applications. 2022
  

• Unseen Anomaly Detection on Networks via Multi-Hpersphere Learning
  Shuang Zhou, Xiao Huang, Ninghao Liu, Qiaoyu Tan, Fu-lai Chung
  SDM, 2022
  

2021

• Adversarial Attacks and Defenses: An Interpretation Perspective
  Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu
  SIGKDD Exploration Newsletter, 2021
  

• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
  Fan Yang, Ninghao Liu, Mengnan Du, Xia Hu
  SIGKDD Exploration Newsletter, 2021
  

• Learning Credible DNNs via Incorporating Prior Knowledge and Model Local Explanation
  Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu
  Knowledge and Information Systems (KAIS), 2021

• ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
  Raj Vardhan, Ninghao Liu, Phakpoom Chinprutthiwong, Weijie Fu, Zhenyu Hu, Xia Ben Hu, Guofei Gu
  preprint, 2021

• Differentiated Explanation of Deep Neural Networks with Skewed Distributions
  Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao, Xia Hu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021

• Dynamic Memory based Attention Network for Sequential Recommendation
  Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu
  AAAI, 2021
  

• Sparse-Interest Network for Sequential Recommendation
  Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu
  WSDM, 2021 (code)
  

2020

• Techniques for Interpretable Machine Learning
  Mengnan Du, Ninghao Liu, Xia Hu
  Communications of the ACM (CACM), 2020
  The Highlighted Article on the cover page of the Janurary 2020 issue, Communications of the ACM.

• Learning Sparse Codes From Compressed Representations With Biologically Plausible Local Wiring Constraints
  Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell
  NeurIPS, 2020
  

• Spam detection in online social media: A survey
  Ninghao Liu, Xia Hu
  Social Media Analytics: Advances and Applications (Jiliang Tang and Charu Aggaral, editors), 2020 (forthcoming with CRC Press).

• Explainable Recommender Systems via Resolving Learning Representations
  Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, Soo-Hyun Choi
  CIKM, 2020
  

• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
  Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang and Xia Hu
  KDD, 2020
  

• Learning to Hash with Graph Neural Networks for Recommender Systems
  Qiaoyu Tan, Ninghao Liu, Hongxia Yang, Jingren Zhou, Xia Hu
  The Web Conference (WWW), 2020 (code)
  

• Deep Neural Networks with Knowledge Instillation
  Fan Yang, Ninghao Liu, Mengnan Du, Kaixiong Zhou, Shuiwang Ji, Xia Hu
  SDM, 2020
  

• A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter
  Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, Xia Hu
  BMC Medical Informatics and Decision Making, 2020

2019

• Deep Representation Learning for Social Network Analysis
  Qiaoyu Tan, Ninghao Liu, Xia Hu
  Frontiers in Big Data, 2019

• Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
  Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu
  KDD, 2019
  [code]

• Learning Credible Deep Neural Networks with Rationale Regularization
  Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu
  ICDM, 2019
  [code]
  Best Paper Award Candidate

• Deep Structured Cross-Modal Anomaly Detection
  Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu
  IJCNN, 2019

• On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
  Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, Xia Hu
  The Web Conference (WWW), 2019
  [code]
  Best Paper Award Shortlist

• Representation Interpretation with Spatial Encoding and Multimodal Analytics
  Ninghao Liu, Mengnan Du, Xia Hu
  WSDM, 2019

• An Interpretable Neural Model with Interactive Stepwise Influence
  Yin Zhang, Ninghao Liu, Shuiwang Ji, James Caverlee, Xia Hu
  PAKDD, 2019

• Identification of Cancer Survivors Living with PTSD on Social Media
  Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, Xia Hu
  Studies in health technology and informatics, 2019

2018

• Towards Interpretation of Recommender Systems with Sorted Explanation Paths
  Fan Yang, Ninghao Liu, Suhang Wang, Xia Hu
  ICDM, 2018

• On Interpretation of Network Embedding via Taxonomy Induction
  Ninghao Liu, Xiao Huang, Jundong Li, Xia Hu
  KDD, 2018
  [code]

• Adversarial Classification with Model Interpretation
  Ninghao Liu, Hongxia Yang, Xia Hu
  KDD, 2018
  [code]

• Towards Explanation of DNN-based Prediction with Guided Feature Inversion
  Mengnan Du, Ninghao Liu, Qingquan Song, Xia Hu
  KDD, 2018
  [code]

• Contextual Outlier Interpretation
  Ninghao Liu, Donghwa Shin, Xia Hu
  IJCAI, 2018
  [code]

2017 and ealier

• Accelerated local anomaly detection via resolving attributed networks
  Ninghao Liu, Xiao Huang, Xia Hu
  IJCAI, 2017
  [code]

• Cortical communication via randomized dimensionality reduction with local synaptic connections (poster)
  Christopher Rozell and Ninghao Liu
  Computational and Systems Neuroscience (COSYNE), 2016

• Distance-weighted backlog differentials for back-pressure routing in multi-hop wireless networks
  Jing Lu, Zuming Huang, Ninghao Liu, Quansheng Guan
  IEEE/CIC International Conference on Communications in China (ICCC), 2014
  Best Paper Award

• Spam Detection on Social Networks
  Ninghao Liu, Xia Hu
  Encyclopedia of Social Network Analysis and Mining, 2017

• An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums
  Jun Gao, Ninghao Liu, Mark Lawley, Xia Hu
  Journal of Healthcare Engineering, 2017

• Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance
  Xia Hu, Peter Reaven, Aramesh Saremi, Ninghao Liu, Mohammed Ali Abbasi, Huan Liu, Raymond Migrino
  EURASIP Journal on Bioinformatics and Systems Biology, 2016

• A deployment method based on spring force in wireless robot sensor networks
  Xiangyu Yu, Ninghao Liu, Xin Qian, Tao Zhang
  International Journal of Advanced Robotic Systems, 2014

• A node deployment algorithm based on van der Waals force in wireless sensor networks
  Xiangyu Yu, Ninghao Liu*, Weipeng Huang, Xin Qian, Tao Zhang
  International Journal of Distributed Sensor Networks, 2014