Selected Publications
“Prize” Papers (best papers, runner-ups, finalists, etc)
Z. Xu, K. Ding, Y. Wang, H. Liu, and H. Tong: Generalized Few-Shot Node Classification. ICDM 2022, (Best-ranked paper of ICDM 2022)
Z. Yu, D. Jin, Z. Liu, D. He, X. Wang, H. Tong, and J. Han: AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks, ICDM 2021, (best student paper runner-up)
Si Zhang, Hanghang Tong, Jiejun Xu, Ross Maciejewski: Graph Convolutional Networks: Algorithms, Applications and Open Challenges [PDF] (bests of CsoNet 2018)
Yaojing Wang, Yuan Yao, Hanghang Tong, Xuan Huo, Min Li, Feng Xu, Jian Lu: Bug Localization via Supervised Topic Modeling. [PDF] (bests of ICDM 2018)
Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia: Rapid Analysis of Network Connectivity. [PDF] (best demo candidate (second place), CIKM 2017)
Robert Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant B. Navathe, Hanghang Tong, Duen Horng Chau: Visual Graph Query Construction and Refinement. [PDF] (best demo, honorable mention, SIGMOD 2017)
Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, Qing He: FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks. [PDF] [Slides] (bests of KDD 2016)
Chen Chen, Hanghang Tong: Fast Eigen-Functions Tracking on Dynamic Graphs. [PDF] [Slides] (bests of SDM 2015)
Rui Liu, Wei Cheng, Hanghang Tong, Wei Wang, Xiang Zhang: Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment. [PDF] (bests of ICDM 2015)
Xing Su, Hanghang Tong and Ping Ji: Activity Recognition with Smartphone Sensors. [PDF] (best paper, IEEE Tsinghua Science and Technology 2014)
Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad, Michalis Faloutsos, Christos Faloutsos: Gelling, and melting, large graphs by edge manipulation. [PDF] [slides] (best paper, CIKM 2012)
Hanghang Tong, Ching-Yung Lin: Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection. [PDF] [Slides] (bests of SDM 2011)
Jingrui He, Hanghang Tong, Jaime G. Carbonell: Rare Category Characterization. [PDF] (bests of ICDM 2010)
Books
Survey, Review and Position Papers
M. Loui, N. Bosch, A. Chan, J. Davis, R. Gutiérrez, J. He, K. Karahalios, S. Koyejo, R. Mendenhall, M. Sanfilippo, H. Tong, L. Varshney, Y. Wang: Artificial Intelligence, Social Responsibility, and the Roles of the University. Communications of the ACM 67 (8), 22-25, 2024
M. Zitnik, M. Li, A. Wells, K. Glass, D. Gysi, A. Krishnan, T. Murali, P. Radivojac, S. Roy, A. Baudot, S. Bozdag, D. Chen, L. Cowen, K. Devkota, A. Gitter, S. Gosline, P. Gu, P. Guzzi, H. Huang, M. Jiang, Z. Kesimoglu, M. Koyuturk, J. Ma, A. Pico, N. Pržulj, T. Przytycka, B. Raphael, A. Ritz, R. Sharan, Y. Shen, M. Singh, D. K Slonim, H. Tong, X. Yang, B. Yoon, H. Yu, T. Milenković: Current and future directions in network biology. Bioinformatics Advances, 2024
H. Zhang, B. Wu, X. Yuan, S. Pan, H. Tong, J. Pei: Trustworthy Graph Neural Networks: Aspects, Methods, and Trends. Proc. IEEE 112(2): 97-139 (2024), (Cover of PIEEE)
L. Zheng, B. Jing, Z. Li, H. Tong, and J. He: Heterogeneous Contrastive Learning for Foundation Models and Beyond. KDD 2024
D. Fu, W. Bao, R. Maciejewski, H. Tong, J. He: Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey. SIGKDD Explor. 25(1): 54-72 (2023)
S., Freitas, D. Yang, S. Kumar, H. Tong, and D. Chau: Graph Vulnerability and Robustness: A Survey. IEEE TKDE 2023
K. Ding, Z. Xu, H. Tong, and H. Liu: Data Augmentation for Deep Graph Learning: A Survey. SIGKDD Explorations 2022
Y. Wang, H. Tong, Z. Zhu and Y. Li: Nested Named Entity Recognition: A Survey. ACM TKDD 16(6): 108:1-108:29 (2022)
Y. Shi, Y. Liu, H. Tong, J. He, G. Yan, N. Cao: Visual Analytics of Anomalous User Behaviors: A Survey. IEEE Trans. Big Data 8(2): 377-396 (2022)
Y. Wang, Y. Yao, H. Tong, F. Xu, J. Lu: A Brief Review of Network Embedding. Big Data Min. Anal. 2(1): 35-47 (2019), (Excellent Paper Award 2021)
Y. Shi, Y. Liu, H. Tong, J. He, G. Yan, and N. Cao: Visual Analytics of Anomalous User Behaviors: A Survey. IEEE Transactions on Big Data 2019
S. Zhang, H. Tong, J. Xu and R. Maciejewski: Graph Convolutional Networks: A Comprehensive Review. Springer - Computational Social Networks 2019, (Bests of CSoNet 2018)
Y. Yao, H. Tong, F. Xu, J. Lu: On the Measurement and Prediction of Web Content Utility. A Review. SIGKDD Explorations 19(2): 1-12 (2017)
X. Su, H. Tong and P. Ji: “Activity Recognition with Smartphone Sensors”, Tsinghua Science and Technology, 19 (3), 235-249, 2014, (Best Paper Award 2015)
L. Akoglu, H. Tong, D. Koutra: “Graph-based Anomaly Detection and Description: A Survey”, DAMI, 29(3): 626-688, 2015
Sample Papers
Z. Liu, R. Qiu, Z. Zeng, Y. Zhu, H. Hamann, H. Tong: AIM: Attributing, Interpreting, Mitigating Data-encoded Unfairness. KDD 2024
J. Wu, H. Tong and J. He: Distributional Network of Networks for Modeling Data Heterogeneity. KDD 2024
R. Qiu, and H. Tong: Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization. ICML 2024
Z. Zeng, R. Qiu, Z. Xu, Z. Liu, Y. Yan, T. Wei, L. Ying, J. He, H. Tong: Graph Mixup on Approximate Gromov–Wasserstein Geodesics. ICML 2024
Z. Liu, R. Qiu, Z. Zeng, H. Yoo, D. Zhou, Z. Xu, Y. Zhu, K. Weldemariam, J. He, H. Tong: Class-Imbalanced Graph Learning without Class Rebalancing. ICML 2024
H. Xu, Y. Yan, D. Wang, Z. Xu, Z. Zeng, T. Abdelzaher, J. Han, and H. Tong : SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter. ICML 2024
Y. Ban, I. Agarwal, Z. Wu, Y. Zhu, K. Weldemariam, H. Tong, J. He: Neural Active Learning Beyond Bandits. ICLR 2024
J. Kang, Y. Xia, R. Maciejewski, J. Luo, H. Tong: Deceptive Fairness Attacks on Graphs via Meta Learning. ICLR 2024
Y. Yan, Y. Hu, Q. Zhou, L. Liu, Z. Zeng, Y. Chen, M. Pan, H. Chen, M. Das, H. Tong: PaCEr: Network Embedding From Positional to Structural. WWW 2024: 2485-2496
H. Yoo, Z. Zeng, J. Kang, R. Qiu, D. Zhou, Z. Liu, F. Wang, C. Xu, E. Chan, H. Tong: Ensuring User-side Fairness in Dynamic Recommender Systems. WWW 2024: 3667-3678
Y. Yan, Y. Chen, H. Chen, M. Xu, M. Das, H. Yang, H. Tong: From Trainable Negative Depth to Edge Heterophily in Graphs. NeurIPS 2023
Y. Yan, B. Jing, L. Liu, R. Wang, J. Li, T. Abdelzaher, H. Tong: Reconciling Competing Sampling Strategies of Network Embedding. NeurIPS 2023
Z. Xu, Y. Chen, Q. Zhou, Y. Wu, M. Pan, H. Yang, H. Tong: Node Classification Beyond Homophily: Towards a General Solution. KDD 2023
Z. Xu, Y. Chen, M. Pan, H. Chen, M. Das, H. Yang, H. Tong: Kernel Ridge Regression-Based Graph Dataset Distillation. KDD 2023
D. Wang, Y. Yan, R. Qiu, Y. Zhu, K. Guan, A. Margenot, H. Tong: Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders. KDD 2023
R. Qiu, D. Wang, L. Ying, H. Poor, Y. Zhang, H. Tong: Reconstructing Graph Diffusion History from a Single Snapshot. KDD 2023
Z. Zeng, R. Zhu, Y. Xia, H. Zeng, and H. Tong: Generative Graph Dictionary Learning. ICML 2023
Z. Zeng, S. Zhang, Y. Xia and H. Tong: PARROT: Position-Aware Regularized Optimal Transport for Network Alignment. WWW 2023
L. Liu, Y. Chen, M. Das, H. Yang and H. Tong: Knowledge Graph Question Answering with Ambiguous Query. WWW 2023
Y. Ko, S. Ryu, S. Han, Y. Jeon, J. Kim, S. Park, K. Han, H. Tong and S. Kim: KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction. WWW 2023
W. Cong, S. Zhang, J. Kang, B. Yuan, H. Wu, X. Zhou, H. Tong, and M. Mahdavi: Do We Really Need Complicated Model Architectures For Temporal Networks? ICLR 2023, (notable-top-5%)
C. Han, Q. He, C. Yu, X. Du, H. Tong, and H. Ji: Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning. ICLR 2023
H. Wang, W. Huang, Z. Wu, H. Tong, A. Margenot, and J. He: Deep Active Learning by Leveraging Training Dynamics. NeuIPS 2022
J. Xie, J. Xu, G. Wang, Y. Yao, Z. Li, C. Cao, and H. Tong: A Deep Learning Data loader with Shared Data Preparation. NeuIPS 2022
H. Li, Y. Weng, and H. Tong: CoNSoLe: Convex Neural Symbolic Learning. NeuIPS 2022
Y. Ban, Y. Zhang, H. Tong, A. Banerjee, and J. He: Improved Algorithms for Neural Active Learning. NeuIPS 2022
Z. Xu, K. Ding, Y. Wang, H. Liu, and H. Tong: Generalized Few-Shot Node Classification. ICDM 2022, (Best-ranked paper of ICDM 2022)
L. Liu, B. Du, J. Xu, Y. Xia and H. Tong: Joint Knowledge Graph Completion and Question Answering. KDD 2022
J. Kang, Q. Zhou and H. Tong: JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. KDD 2022
J. Liu, Z. Li, Y. Yao, F. Xu, X. Ma, M. Xu and H. Tong: Fair Representation Learning: An Alternative to Mutual Information. KDD 2022
H. Li, H. Tong and Y. Weng: Domain Adaptation in Physical Systems via Graph Kernel. KDD 2022
Z. Xu, B. Du and H. Tong: Graph Sanitation with Application to Node Classification. WWW 2022: 1136-1147
B. Li, B. Jing and H. Tong: Graph Communal Contrastive Learning. WWW 2022: 1203-1213
J. Kang, Y. Zhu, Y. Xia, J. and H. Tong: RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network. WWW 2022: 1214-1225
S. Feng, B. Jing, Y. Zhu and H. Tong: Adversarial Graph Contrastive Learning with Information Regularizer. WWW 2022: 1362-1371
Z. Yu, D. Jin, Z. Liu, D. He, X. Wang, H. Tong, and J. Han: AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks, ICDM 2021, (best student paper runner-up)
Boxin Du, Lihui Liu, Hanghang Tong: Sylvester Tensor Equation for Multi-Way Association. KDD 2021 [PDF] [Slides]
Si Zhang, Hanghang Tong, Long Jin, Yinglong Xia, Yunsong Guo: Balancing Consistency and Disparity in Network Alignment. KDD 2021. [PDF] [Slides]
Lihui Liu, Boxin Du, Heng Ji, ChengXiang Zhai, Hanghang Tong: Neural-Answering Logical Queries on Knowledge Graphs. KDD 2021. [PDF] [Slides] [Code]
Lihui Liu, Boxin Du, Yi Ren Fung, Heng Ji, Jiejun Xu, Hanghang Tong: KompaRe: A Knowledge Graph Comparative Reasoning System. KDD 2021. [PDF] [Slides] [Code]
Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li: Individual Fairness for Graph Neural Networks: A Ranking based Approach. KDD 2021 [PDF]
Baoyu Jing, Chanyoung Park, Hanghang Tong: HDMI: High-order Deep Multiplex Infomax. WWW 2021 [PDF] [Slides] [Code]
Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu: Few-shot Network Anomaly Detection via Cross-network Meta-learning. WWW 2021 [PDF] [Slides] [Code]
Qinghai Zhou, Liangyue Li, Xintao Wu, Nan Cao, Lei Ying, Hanghang Tong: Attent: Active Attributed Network Alignment. WWW 2021 [PDF] [Slides] [Code]
BRIGHT: A Bridging Algorithm for Network Alignment. WWW 2021 [PDF] [Slides] [Data and Code]
Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong: InFoRM: Individual Fairness on Graph Mining. KDD 2020 [PDF] [Slides]
Si Zhang, Hanghang Tong, Yinglong Xia, Liang Xiong, Jiejun Xu: NetTrans: Neural Cross-Network Transformation. KDD 2020 [PDF][Slides]
Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong: Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering. NeurIPS 2020 [PDF]
Yaojing Wang, Guosheng Pan, Yuan Yao, Hanghang Tong, Hongxia Yang, Feng Xu, Jian Lu: Bringing Order to Network Embedding: A Relative Ranking based Approach. CIKM 2020 [PDF]
Pei Yang, Qi Tan, Hanghang Tong, Jingrui He: Task-Adversarial Co-Generative Nets. KDD 2019. [PDF]
Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong: PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. NeurIPS 2019. [PDF]
Rui Zhang, Hanghang Tong: Robust Principal Component Analysis with Adaptive Neighbors. NeurIPS 2019. [PDF]
Jian Kang, Hanghang Tong: N2N: Network Derivative Mining. CIKM 2019. [PDF]
Si Zhang, Hanghang Tong, Ross Maciejewski, Tina Eliassi-Rad: Multilevel Network Alignment. WWW 2019. [PDF]
Chen Chen, Ruiyue Peng, Lei Ying, Hanghang Tong: Network Connectivity Optimization: Fundamental Limits and Effective Algorithms. KDD 2018 [PDF]
Boxin Du, Hanghang Tong: FASTEN: Fast Sylvester Equation Solver for Graph Mining. KDD 2018. [PDF]
Liangyue Li, Hanghang Tong, Yong Wang, Conglei Shi, Nan Cao, Norbou Buchler: Is the Whole Greater Than the Sum of Its Parts? KDD 2017. [PDF] [Slides]
Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu: HoORaYs: High-order Optimization of Rating Distance for Recommender Systems. KDD 2017. [PDF]
Dawei Zhou, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, Jingrui He: A Local Algorithm for Structure-Preserving Graph Cut. KDD 2017. [PDF]
Boxin Du, Si Zhang, Nan Cao, Hanghang Tong: FIRST: Fast Interactive Attributed Subgraph Matching. KDD 2017. [PDF]
Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, Qing He: FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks. KDD 2016. [PDF] [Slides] (bests of kdd2016)
Liangyue Li, Yuan Yao, Jie Tang, Wei Fan, Hanghang Tong: QUINT: On Query-Specific Optimal Networks. KDD 2016. [PDF] [Slides]] [Code]
Liangyue Li, Hanghang Tong: The Child is Father of the Man: Foresee the Success at the Early Stage. KDD 2015. [PDF] [Slides]
Yongjie Cai, Hanghang Tong, Wei Fan, Ping Ji, Qing He: Facets: Fast Comprehensive Mining of Coevolving High-order Time Series. KDD 2015. [PDF]
Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang: Flexible and Robust Multi-Network Clustering. KDD 2015. [PDF]
Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juanzi Li: Panther: Fast Top-k Similarity Search on Large Networks. KDD 2015. [PDF]
Liangyue Li, Hanghang Tong, Nan Cao, Kate Ehrlich, Yu-Ru Lin, Norbou Buchler. Replacing the Irreplaceable: Fast Algorithm for Team Member Recommendation. WWW 2015. [PDF]
Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu: Predicting long-term impact of CQA posts: a comprehensive viewpoint. KDD 2014. [PDF] [Slides] [Data and Code]
Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang: Inside the atoms: ranking on a network of networks. KDD 2014. [PDF]
Danai Koutra, Hanghang Tong, David Lubensky: BIG-ALIGN: Fast Bipartite Graph Alignment. ICDM 2013. [PDF] [slides]
Yuan Yao, Hanghang Tong, Xifeng Yan, Feng Xu, Jian Lu: MATRI: a multi-aspect and transitive trust inference model. WWW 2013. [PDF] [Slides]
Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad, Michalis Faloutsos, Christos Faloutsos: Gelling, and melting, large graphs by edge manipulation. CIKM 2012. [PDF] [slides] (best paper award)
Hanghang Tong, Jingrui He, Zhen Wen, Ravi Konuru, Ching-Yung Lin: Diversified ranking on large graphs: an optimization viewpoint. KDD 2011 [PDF] [Slides]
U. Kang, Hanghang Tong, Jimeng Sun, Ching-Yung Lin, Christos Faloutsos: GBASE: a scalable and general graph management system. KDD 2011 [PDF]
Hanghang Tong, B. Aditya Prakash, Charalampos E. Tsourakakis, Tina Eliassi-Rad, Christos Faloutsos, Duen Horng Chau: On the Vulnerability of Large Graphs. ICDM 2010. [PDF] [Slides]
Kensuke Onuma, Hanghang Tong, Christos Faloutsos: TANGENT: a novel, 'Surprise me’, recommendation algorithm. KDD 2009. [PDF]
Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, Philip S. Yu, Christos Faloutsos: Colibri: fast mining of large static and dynamic graphs. KDD 2008. [PDF] [Slides]
Hanghang Tong, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos: Proximity Tracking on Time-Evolving Bipartite Graphs. SDM 2008. [PDF] [Slides] (best paper award)
Hanghang Tong, Christos Faloutsos, Brian Gallagher, Tina Eliassi-Rad: Fast best-effort pattern matching in large attributed graphs. KDD 2007.[PDF] [Slides] [Code] [Video]
Hanghang Tong, Christos Faloutsos, Yehuda Koren: Fast direction-aware proximity for graph mining. KDD 2007. [PDF] [Slides] [Video] [Correction]
Hanghang Tong, Christos Faloutsos, Jia-Yu Pan: Fast Random Walk with Restart and Its Applications. ICDM 2006.[PDF] [Slides] [Code] (best research paper award)
Hanghang Tong, Christos Faloutsos: Center-piece subgraphs: problem definition and fast solutions. KDD 2006. [PDF] [Slides] [data and code]
Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying Ma, Changshui Zhang, HongJiang Zhang: A Unified Optimization Based Learning Method for Image Retrieval. CVPR 2005. [PDF]
Hanghang Tong, Mingjing Li, HongJiang Zhang, Changshui Zhang: Blur detection for digital images using wavelet transform. ICME 2004. [PDF]