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ORCID28th KDD 2022: Washington, DC, USA
- Aidong Zhang, Huzefa Rangwala:
KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. ACM 2022, ISBN 978-1-4503-9385-0
Keynote Talks
- Lise Getoor:
The Power of (Statistical) Relational Thinking. 1 - Milind Tambe:
AI for Social Impact: Results from Deployments for Public Health and Conversation. 2 - Shang-Hua Teng:
Beyond Traditional Characterizations in the Age of Data: Big Models, Scalable Algorithms, and Meaningful Solutions. 3
Research Track Full Papers
- Sarp Aykent, Tian Xia:
GBPNet: Universal Geometric Representation Learning on Protein Structures. 4-14 - Guangji Bai, Liang Zhao:
Saliency-Regularized Deep Multi-Task Learning. 15-25 - Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang:
Submodular Feature Selection for Partial Label Learning. 26-34 - Maciej Besta, Raphael Grob, Cesare Miglioli
, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler:
Motif Prediction with Graph Neural Networks. 35-45 - Di Chai, Leye Wang, Junxue Zhang, Liu Yang, Shuowei Cai, Kai Chen, Qiang Yang:
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data. 46-55 - Deepayan Chakrabarti:
Avoiding Biases due to Similarity Assumptions in Node Embeddings. 56-65 - Mohna Chakraborty, Adithya Kulkarni, Qi Li:
Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction. 66-75 - Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran:
Multi-Variate Time Series Forecasting on Variable Subsets. 76-86 - Jiayi Chen, Aidong Zhang:
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. 87-96 - Jin Chen, Guanyu Ye, Yan Zhao, Shuncheng Liu, Liwei Deng, Xu Chen, Rui Zhou, Kai Zheng:
Efficient Join Order Selection Learning with Graph-based Representation. 97-107 - Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang:
Knowledge-enhanced Black-box Attacks for Recommendations. 108-117 - Liyi Chen, Zhi Li, Tong Xu, Han Wu, Zhefeng Wang, Nicholas Jing Yuan, Enhong Chen:
Multi-modal Siamese Network for Entity Alignment. 118-126 - Man-Sheng Chen, Chang-Dong Wang, Dong Huang, Jian-Huang Lai, Philip S. Yu:
Efficient Orthogonal Multi-view Subspace Clustering. 127-135 - Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun:
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank. 136-145 - Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou, Liang Sun:
Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting. 146-156 - Xingguang Chen, Fangyuan Zhang, Sibo Wang:
Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling. 157-167 - Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma, Ruiming Tang, Jingjie Li, Irwin King:
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation. 168-178 - Kewei Cheng, Jiahao Liu, Wei Wang, Yizhou Sun:
RLogic: Recursive Logical Rule Learning from Knowledge Graphs. 179-189 - Zhi Cheng, Xiu Su, Xueyu Wang, Shan You, Chang Xu:
Sufficient Vision Transformer. 190-200 - Eli Chien, Puoya Tabaghi, Olgica Milenkovic:
HyperAid: Denoising in Hyperbolic Spaces for Tree-fitting and Hierarchical Clustering. 201-211 - Ranak Roy Chowdhury, Xiyuan Zhang, Jingbo Shang, Rajesh K. Gupta, Dezhi Hong:
TARNet: Task-Aware Reconstruction for Time-Series Transformer. 212-220 - Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. 221-230 - Qianhao Cong, Jing Tang, Kai Han, Yuming Huang, Lei Chen, Yeow Meng Chee:
Noisy Interactive Graph Search. 231-240 - Sen Cui, Jian Liang, Weishen Pan, Kun Chen, Changshui Zhang, Fei Wang:
Collaboration Equilibrium in Federated Learning. 241-251 - Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Rui Zhang, Jie Sun:
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. 252-262 - Sebastian Dalleiger, Jilles Vreeken:
Discovering Significant Patterns under Sequential False Discovery Control. 263-272 - Khalil Damak, Sami Khenissi, Olfa Nasraoui:
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers. 273-282 - Debanjan Datta, Feng Chen, Naren Ramakrishnan:
Framing Algorithmic Recourse for Anomaly Detection. 283-293 - Songgaojun Deng, Huzefa Rangwala, Yue Ning:
Robust Event Forecasting with Spatiotemporal Confounder Learning. 294-304 - Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, Yongdong Zhang:
Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. 305-315 - Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li:
On Structural Explanation of Bias in Graph Neural Networks. 316-326 - Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach:
Fair Labeled Clustering. 327-335 - Chenguang Fang, Shaoxu Song, Yinan Mei, Ye Yuan, Jianmin Wang:
On Aligning Tuples for Regression. 336-346 - Ziquan Fang, Yuntao Du, Xinjun Zhu, Danlei Hu, Lu Chen, Yunjun Gao, Christian S. Jensen:
Spatio-Temporal Trajectory Similarity Learning in Road Networks. 347-356 - Kaituo Feng, Changsheng Li, Ye Yuan, Guoren Wang:
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks. 357-366 - Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I. Torvik, Jingrui He:
Meta-Learned Metrics over Multi-Evolution Temporal Graphs. 367-377 - Tianfan Fu, Jimeng Sun:
SIPF: Sampling Method for Inverse Protein Folding. 378-388 - Tianfan Fu, Jimeng Sun:
Antibody Complementarity Determining Regions (CDRs) design using Constrained Energy Model. 389-399 - Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán:
Optimal Interpretable Clustering Using Oblique Decision Trees. 400-410 - Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin Ding:
Finding Meta Winning Ticket to Train Your MAML. 411-420 - Yunjun Gao, Xiaoze Liu, Junyang Wu, Tianyi Li, Pengfei Wang, Lu Chen:
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities. 421-431 - Yuyang Gao, Tong Steven Sun, Guangji Bai, Siyi Gu, Sungsoo Ray Hong, Liang Zhao:
RES: A Robust Framework for Guiding Visual Explanation. 432-442 - Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen:
Disentangled Ontology Embedding for Zero-shot Learning. 443-453 - Ehsan Gholami, Mohammad Motamedi, Ashwin Aravindakshan:
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions. 454-464 - Rahul Ghosh, Arvind Renganathan, Kshitij Tayal, Xiang Li, Ankush Khandelwal, Xiaowei Jia, Christopher J. Duffy, John Nieber, Vipin Kumar:
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology. 465-474 - Xingzhi Guo, Baojian Zhou, Steven Skiena:
Subset Node Anomaly Tracking over Large Dynamic Graphs. 475-485 - Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola:
BLISS: A Billion scale Index using Iterative Re-partitioning. 486-495 - Vinayak Gupta, Srikanta Bedathur:
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences. 496-504 - Seok-Ju Hahn, Minwoo Jeong, Junghye Lee:
Connecting Low-Loss Subspace for Personalized Federated Learning. 505-515 - Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong:
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction. 516-524 - Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li:
Streaming Hierarchical Clustering Based on Point-Set Kernel. 525-533 - Huarui He, Jie Wang, Zhanqiu Zhang, Feng Wu:
Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation. 534-544 - Shuo He, Lei Feng, Fengmao Lv, Wen Li, Guowu Yang:
Partial Label Learning with Semantic Label Representations. 545-553 - Wenchong He, Zhe Jiang, Marcus Kriby, Yiqun Xie, Xiaowei Jia, Da Yan, Yang Zhou:
Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery. 554-564 - Catherine F. Higham, Desmond J. Higham, Francesco Tudisco:
Core-periphery Partitioning and Quantum Annealing. 565-573 - Dat Hong, Alberto Maria Segre, Tong Wang:
AdaAX: Explaining Recurrent Neural Networks by Learning Automata with Adaptive States. 574-584 - Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen:
Towards Universal Sequence Representation Learning for Recommender Systems. 585-593 - Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang:
GraphMAE: Self-Supervised Masked Graph Autoencoders. 594-604 - Jiaxin Huang, Yu Meng, Jiawei Han:
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation. 605-614 - Zijian Huang, Meng-Fen Chiang, Wang-Chien Lee:
LinE: Logical Query Reasoning over Hierarchical Knowledge Graphs. 615-625 - Alexis Huet, José Manuel Navarro, Dario Rossi:
Local Evaluation of Time Series Anomaly Detection Algorithms. 635-645 - Bo Hui, Wei-Shinn Ku:
Low-rank Nonnegative Tensor Decomposition in Hyperbolic Space. 646-654 - Md. Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian:
Global Self-Attention as a Replacement for Graph Convolution. 655-665 - Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder:
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles. 666-675 - Roshni G. Iyer, Yunsheng Bai, Wei Wang, Yizhou Sun:
Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs. 676-686 - Yingsheng Ji, Zheng Zhang, Xinlei Tang, Jiachen Shen, Xi Zhang, Guangwen Yang:
Detecting Cash-out Users via Dense Subgraphs. 687-697 - Shengmin Jin, Hao Tian, Jiayu Li, Reza Zafarani:
A Spectral Representation of Networks: The Path of Subgraphs. 698-708 - Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang:
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective. 709-719 - Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin:
Condensing Graphs via One-Step Gradient Matching. 720-730 - Yilun Jin, Kai Chen, Qiang Yang:
Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting. 731-741 - Jian Kang, Qinghai Zhou, Hanghang Tong:
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. 742-752 - Matti Karppa, Rasmus Pagh:
HyperLogLogLog: Cardinality Estimation With One Log More. 753-761 - Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, Jihoon Cho:
SOS: Score-based Oversampling for Tabular Data. 762-772 - Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis:
CoRGi: Content-Rich Graph Neural Networks with Attention. 773-783 - Sehoon Kim, Sheng Shen, David Thorsley, Amir Gholami, Woosuk Kwon, Joseph Hassoun, Kurt Keutzer:
Learned Token Pruning for Transformers. 784-794 - Seonggyeom Kim, Dong-Kyu Chae:
ExMeshCNN: An Explainable Convolutional Neural Network Architecture for 3D Shape Analysis. 795-803 - Yeachan Kim, Bonggun Shin:
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning. 804-812 - Furkan Kocayusufoglu, Arlei Silva, Ambuj K. Singh:
FlowGEN: A Generative Model for Flow Graphs. 813-823 - Danning Lao, Xinyu Yang, Qitian Wu, Junchi Yan:
Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation. 824-834 - Xiaoliang Lei, Hao Mei, Bin Shi, Hua Wei:
Modeling Network-level Traffic Flow Transitions on Sparse Data. 835-845 - Collin Leiber, Lena G. M. Bauer, Michael Neumayr, Claudia Plant, Christian Böhm:
The DipEncoder: Enforcing Multimodality in Autoencoders. 846-856 - Han Li, Dan Zhao, Jianyang Zeng:
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction. 857-867 - Haoran Li, Hanghang Tong, Yang Weng:
Domain Adaptation in Physical Systems via Graph Kernel. 868-876 - Jiajun Li, Zhewei Wei, Bolin Ding, Xiening Dai, Lu Lu, Jingren Zhou:
Sampling-based Estimation of the Number of Distinct Values in Distributed Environment. 893-903 - Jiatong Li, Fei Wang, Qi Liu, Mengxiao Zhu, Wei Huang, Zhenya Huang, Enhong Chen, Yu Su, Shijin Wang:
HierCDF: A Bayesian Network-based Hierarchical Cognitive Diagnosis Framework. 904-913 - Junyi Li, Jian Pei, Heng Huang:
Communication-Efficient Robust Federated Learning with Noisy Labels. 914-924 - Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He:
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN. 925-935 - Rongfan Li, Ting Zhong, Xinke Jiang, Goce Trajcevski, Jin Wu, Fan Zhou:
Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning. 936-944 - Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee:
PAC-Wrap: Semi-Supervised PAC Anomaly Detection. 945-955 - Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui:
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning. 956-966 - Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui:
Transfer Learning based Search Space Design for Hyperparameter Tuning. 967-977 - Yinghao Li, Le Song, Chao Zhang:
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition. 978-988 - Lu Lin, Ethan Blaser, Hongning Wang:
Graph Structural Attack by Perturbing Spectral Distance. 989-998 - Sikun Lin, Shuyun Tang, Scott T. Grafton, Ambuj K. Singh:
Deep Representations for Time-varying Brain Datasets. 999-1009 - Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao:
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. 1010-1020 - Aiwei Liu, Xuming Hu, Li Lin, Lijie Wen:
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph. 1021-1030 - Chengchang Liu, Shuxian Bi, Luo Luo, John C. S. Lui:
Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality. 1031-1041 - Dachuan Liu, Jin Wang, Shuo Shang, Peng Han:
MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. 1042-1050 - Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan, Zhong Ming:
User-Event Graph Embedding Learning for Context-Aware Recommendation. 1051-1059 - Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Adelaide Woicik, Sheng Wang:
Graph-in-Graph Network for Automatic Gene Ontology Description Generation. 1060-1068 - Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang:
Graph Rationalization with Environment-based Augmentations. 1069-1078 - Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Junjie Sun, Hong Yu, Xianchao Zhang:
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection. 1079-1087 - Ji Liu, Zenan Li, Yuan Yao, Feng Xu, Xiaoxing Ma, Miao Xu, Hanghang Tong:
Fair Representation Learning: An Alternative to Mutual Information. 1088-1097 - Lihui Liu, Boxin Du, Jiejun Xu, Yinglong Xia, Hanghang Tong:
Joint Knowledge Graph Completion and Question Answering. 1098-1108 - Qu Liu, Tingjian Ge:
RL2: A Call for Simultaneous Representation Learning and Rule Learning for Graph Streams. 1109-1119 - Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang:
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries. 1120-1130 - Yang Liu, Xiang Ao, Fuli Feng, Qing He:
UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs. 1131-1140 - Yaxu Liu, Jui-Nan Yen, Bo-Wen Yuan, Rundong Shi, Peng Yan, Chih-Jen Lin:
Practical Counterfactual Policy Learning for Top-K Recommendations. 1141-1151 - Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang, Luoyi Fu, Xinbing Wang:
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation. 1152-1161 - Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang:
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer. 1162-1172 - Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh:
Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams. 1173-1182 - Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu:
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? 1183-1191 - Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian:
Learning Differential Operators for Interpretable Time Series Modeling. 1192-1201