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ORCID27th KDD 2021: Virtual Event, Singapore
- Feida Zhu, Beng Chin Ooi, Chunyan Miao:
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021. ACM 2021, ISBN 978-1-4503-8332-5
Keynote Talks
- Vincent Conitzer:
Automated Mechanism Design for Strategic Classification: Abstract for KDD'21 Keynote Talk. 1 - Sharon C. Glotzer:
Data Science for Assembly Engineering. 2 - Claire J. Tomlin:
Safe Learning in Robotics. 3 - Jeffrey D. Ullman:
On the Nature of Data Science. 4
Research Track Papers
- Yanqing An, Qi Liu, Han Wu, Kai Zhang, Linan Yue, Mingyue Cheng, Hongke Zhao, Enhong Chen
:
LawyerPAN: A Proficiency Assessment Network for Trial Lawyers. 5-13 - Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister:
Fine-Grained System Identification of Nonlinear Neural Circuits. 14-24 - Bing Bai, Jian Liang, Guanhua Zhang, Hao Li, Kun Bai, Fei Wang:
Why Attentions May Not Be Interpretable? 25-34 - Yikun Ban, Jingrui He, Curtiss B. Cook:
Multi-facet Contextual Bandits: A Neural Network Perspective. 35-45 - Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang:
Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization. 46-54 - Artem Betlei, Eustache Diemert, Massih-Reza Amini:
Uplift Modeling with Generalization Guarantees. 55-65 - Arindam Bhattacharya, Sumanth Varambally, Amitabha Bagchi, Srikanta Bedathur:
Fast One-class Classification using Class Boundary-preserving Random Projections. 66-74 - Martin Bompaire, Alexandre Gilotte, Benjamin Heymann:
Causal Models for Real Time Bidding with Repeated User Interactions. 75-85 - Alexander Braylan, Matthew Lease:
Aggregating Complex Annotations via Merging and Matching. 86-94 - Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? 95-105 - Hongjie Chen, Ryan A. Rossi, Kanak Mahadik, Sungchul Kim, Hoda Eldardiry:
Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation. 106-116 - Huiping Chen, Alessio Conte, Roberto Grossi, Grigorios Loukides, Solon P. Pissis, Michelle Sweering:
On Breaking Truss-Based Communities. 117-126 - Junjie Chen, Wendy Hui Wang, Hongchang Gao, Xinghua Shi:
PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks. 127-137 - Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, Meng Wang:
Learning Elastic Embeddings for Customizing On-Device Recommenders. 138-147 - Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu:
Causal Understanding of Fake News Dissemination on Social Media. 148-157 - Sohee Cho, Wonjoon Chang, Ginkyeng Lee, Jaesik Choi:
Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes. 158-166 - Zhendong Chu, Hongning Wang:
Improve Learning from Crowds via Generative Augmentation. 167-175 - Zhixuan Chu, Stephen L. Rathbun, Sheng Li:
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data. 176-184 - Corinna Coupette, Jilles Vreeken:
Graph Similarity Description: How Are These Graphs Similar? 185-195 - Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato:
Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages. 196-206 - Sen Cui, Weishen Pan, Changshui Zhang, Fei Wang:
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility. 207-217 - Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang:
Labeled Data Generation with Inexact Supervision. 218-226 - Enyan Dai, Charu Aggarwal, Suhang Wang:
NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs. 227-236 - Arka Daw, M. Maruf, Anuj Karpatne:
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics. 237-247 - Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb:
MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification. 248-257 - Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu:
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution. 258-268 - Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang:
ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting. 269-278 - Yuhui Ding, Quanming Yao, Huan Zhao, Tong Zhang:
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. 279-288 - Jialin Dong, Da Zheng, Lin F. Yang, George Karypis:
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. 289-299 - Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li:
Individual Fairness for Graph Neural Networks: A Ranking based Approach. 300-310 - Boxin Du, Lihui Liu, Hanghang Tong:
Sylvester Tensor Equation for Multi-Way Association. 311-321 - Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Jiang Zhang, Shi Han, Dongmei Zhang:
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data. 322-331 - Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer:
When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods. 332-341 - Jicong Fan:
Large-Scale Subspace Clustering via k-Factorization. 342-352 - Jinyuan Fang, Shangsong Liang, Zaiqiao Meng, Qiang Zhang:
Gaussian Process with Graph Convolutional Kernel for Relational Learning. 353-363 - Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie:
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. 364-373 - Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu:
Multiple-Instance Learning from Similar and Dissimilar Bags. 374-382 - Jonas Fischer, Jilles Vreeken:
Differentiable Pattern Set Mining. 383-392 - Tao-Yang Fu, Wang-Chien Lee:
ProgRPGAN: Progressive GAN for Route Planning. 393-403 - Tianfan Fu, Cao Xiao, Cheng Qian, Lucas M. Glass, Jimeng Sun:
Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery. 404-414 - Chen Gao, Quanming Yao, Depeng Jin, Yong Li:
Efficient Data-specific Model Search for Collaborative Filtering. 415-425 - Ji Gao, Xiao Huang, Jundong Li:
Unsupervised Graph Alignment with Wasserstein Distance Discriminator. 426-435 - David García-Soriano, Francesco Bonchi:
Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints. 436-446 - Negin Golrezaei, Max Lin, Vahab S. Mirrokni, Hamid Nazerzadeh:
Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders. 447-457 - Ludmila Gordeeva, Vasily Ershov, Oleg Gulyaev, Igor Kuralenok:
Meaning Error Rate: ASR domain-specific metric framework. 458-466 - Jiewei Gu, Weiguo Zheng, Yuzheng Cai, Peng Peng:
Towards Computing a Near-Maximum Weighted Independent Set on Massive Graphs. 467-477 - Xiaotao Gu, Zihan Wang, Zhenyu Bi, Yu Meng, Liyuan Liu, Jiawei Han, Jingbo Shang:
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging. 478-486 - Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Qi Zhang, Feng Kuang, Gao-Le Li, Zhang-Xun Liu, Guobin Wu, Nan Ma, Qun Li, Yu-Feng Li:
Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment. 487-495 - Wei Guo, Rong Su, Renhao Tan, Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, Xiuqiang He:
Dual Graph enhanced Embedding Neural Network for CTR Prediction. 496-504 - Xiaojie Guo, Yuanqi Du, Liang Zhao:
Deep Generative Models for Spatial Networks. 505-515 - Xingzhi Guo, Baojian Zhou, Steven Skiena:
Subset Node Representation Learning over Large Dynamic Graphs. 516-526 - Nilesh Gupta, Sakina Bohra, Yashoteja Prabhu, Saurabh Purohit, Manik Varma:
Generalized Zero-Shot Extreme Multi-label Learning. 527-535 - Mahdi Hajiabadi, Jasbir Singh, Venkatesh Srinivasan, Alex Thomo:
Graph Summarization with Controlled Utility Loss. 536-546 - Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, Hui Xiong:
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting. 547-555 - Peng Han, Jin Wang, Di Yao, Shuo Shang, Xiangliang Zhang:
A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks. 556-564 - Xueting Han, Zhenhuan Huang, Bang An, Jing Bai:
Adaptive Transfer Learning on Graph Neural Networks. 565-574 - Bing He, Mustaque Ahamad, Srijan Kumar:
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models. 575-584 - Xiaoxi He, Dawei Gao, Zimu Zhou, Yongxin Tong, Lothar Thiele:
Pruning-Aware Merging for Efficient Multitask Inference. 585-595 - Yue He, Peng Cui, Zheyan Shen, Renzhe Xu, Furui Liu, Yong Jiang:
DARING: Differentiable Causal Discovery with Residual Independence. 596-605 - Amin Heyrani Nobari, Wei Chen, Faez Ahmed:
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design. 606-616 - Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko H. Dodge, Jiayu Zhou:
Federated Adversarial Debiasing for Fair and Transferable Representations. 617-627 - Yibo Hu, Latifur Khan:
Uncertainty-Aware Reliable Text Classification. 628-636 - Yun Hua, Xiangfeng Wang, Bo Jin, Wenhao Li, Junchi Yan, Xiaofeng He, Hongyuan Zha:
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem. 637-645 - Han Huang, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, Hui Xiong:
Representation Learning on Knowledge Graphs for Node Importance Estimation. 646-655 - Hao Huang, Yanan Peng, Ting Gan, Weiping Tu, Ruiting Zhou, Sai Wu:
Metric Learning via Penalized Optimization. 656-664 - Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, Jie Tang:
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems. 665-674 - Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, Min Zhou:
Scaling Up Graph Neural Networks Via Graph Coarsening. 675-684 - Zexi Huang, Arlei Silva, Ambuj K. Singh:
A Broader Picture of Random-walk Based Graph Embedding. 685-695 - Zhenya Huang, Xin Lin, Hao Wang, Qi Liu, Enhong Chen, Jianhui Ma, Yu Su, Wei Tong:
DisenQNet: Disentangled Representation Learning for Educational Questions. 696-704 - Zijie Huang, Yizhou Sun, Wei Wang:
Coupled Graph ODE for Learning Interacting System Dynamics. 705-715 - Bo Hui, Da Yan, Haiquan Chen, Wei-Shinn Ku:
TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction. 716-724 - Jun-Gi Jang, U Kang:
Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries. 725-735 - Sheo Yon Jhin, Minju Jo, Taeyong Kong, Jinsung Jeon, Noseong Park:
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations. 736-745 - Meng Jiang:
Cross-Network Learning with Partially Aligned Graph Convolutional Networks. 746-755 - Xunqiang Jiang, Tianrui Jia, Yuan Fang, Chuan Shi, Zhe Lin, Hui Wang:
Pre-training on Large-Scale Heterogeneous Graph. 756-766 - Zhe Jiang, Wenchong He, Marcus Stephen Kirby, Sultan Asiri, Da Yan:
Weakly Supervised Spatial Deep Learning based on Imperfect Vector Labels with Registration Errors. 767-775 - Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, Yang Zhou:
Towards a Better Understanding of Linear Models for Recommendation. 776-785 - Jaehun Jung, Jinhong Jung, U Kang:
Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion. 786-795 - Shizuo Kaji, Akira Horiguchi, Takuro Abe, Yohsuke Watanabe:
A Hyper-surface Arrangement Model of Ranking Distributions. 796-804 - Dimitris Kalimeris, Smriti Bhagat, Shankar Kalyanaraman, Udi Weinsberg:
Preference Amplification in Recommender Systems. 805-815 - SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu:
Topology Distillation for Recommender System. 829-839 - Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi:
Learning to Embed Categorical Features without Embedding Tables for Recommendation. 840-850 - Paris A. Karakasis, Aritra Konar, Nicholas D. Sidiropoulos:
Joint Graph Embedding and Alignment with Spectral Pivot. 851-859 - Vijay Keswani, L. Elisa Celis:
Auditing for Diversity Using Representative Examples. 860-870 - Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe:
Q-Learning Lagrange Policies for Multi-Action Restless Bandits. 871-881 - Nicolas Klodt, Lars Seifert, Arthur Zahn, Katrin Casel, Davis Issac, Tobias Friedrich:
A Color-blind 3-Approximation for Chromatic Correlation Clustering and Improved Heuristics. 882-891 - Runze Lei, Pinghui Wang, Rundong Li, Peng Jia, Junzhou Zhao, Xiaohong Guan, Chao Deng:
Fast Rotation Kernel Density Estimation over Data Streams. 892-902 - Collin Leiber, Lena G. M. Bauer, Benjamin Schelling, Christian Böhm, Claudia Plant:
Dip-based Deep Embedded Clustering with k-Estimation. 903-913 - Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, Noseong Park:
Large-Scale Data-Driven Airline Market Influence Maximization. 914-924 - Haoran Li, Yang Weng:
Physical Equation Discovery Using Physics-Consistent Neural Network (PCNN) Under Incomplete Observability. 925-933 - Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. 934-942 - Jiayu Li, Hongyu Lu, Chenyang Wang, Weizhi Ma, Min Zhang, Xiangyu Zhao, Wei Qi, Yiqun Liu, Shaoping Ma:
A Difficulty-Aware Framework for Churn Prediction and Intervention in Games. 943-952 - Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai, Xiao-Ming Wu:
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs. 953-963 - Shiju Li, Xin Huang, Chul-Ho Lee:
An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs. 964-974 - Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong:
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity. 975-985 - Tianbo Li, Tianze Luo, Yiping Ke, Sinno Jialin Pan:
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions. 986-994 - Xin-Chun Li, De-Chuan Zhan:
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. 995-1005 - Xuejun Liao, Patrick Koch, Shunping Huang, Yan Xu:
Efficient Collaborative Filtering via Data Augmentation and Step-size Optimization. 1006-1016 - Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian:
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport. 1017-1026 - Yi-Shan Lin, Wen-Chuan Lee, Z. Berkay Celik:
What Do You See?: Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors. 1027-1035 - Bingyu Liu, Yuhong Guo, Jianan Jiang, Jian Tang, Weihong Deng:
Multi-view Correlation based Black-box Adversarial Attack for 3D Object Detection. 1036-1044 - Brian Liu, Miaolan Xie, Madeleine Udell:
ControlBurn: Feature Selection by Sparse Forests. 1045-1054 - Danyang Liu, Jianxun Lian, Zheng Liu, Xiting Wang, Guangzhong Sun, Xing Xie:
Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning. 1055-1065 - Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, Wenwu Zhu:
Signed Graph Neural Network with Latent Groups. 1066-1075 - Jialu Liu, Tianqi Liu, Cong Yu:
NewsEmbed: Modeling News through Pre-trained Document Representations. 1076-1086 - Lihui Liu, Boxin Du, Heng Ji, ChengXiang Zhai, Hanghang Tong:
Neural-Answering Logical Queries on Knowledge Graphs. 1087-1097 - Qi Liu, Jin Zhang, Defu Lian, Yong Ge, Jianhui Ma, Enhong Chen:
Online Additive Quantization. 1098-1108 - Zemin Liu, Trung-Kien Nguyen, Yuan Fang:
Tail-GNN: Tail-Node Graph Neural Networks. 1109-1119 - Zhuo Liu, Yanxuan Li, Xingzhi Sun, Fei Wang, Gang Hu, Guotong Xie:
Dialogue Based Disease Screening Through Domain Customized Reinforcement Learning. 1120-1128 - Qingqing Long, Lingjun Xu, Zheng Fang, Guojie Song:
HGK-GNN: Heterogeneous Graph Kernel based Graph Neural Networks. 1129-1138 - Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu:
Leveraging Latent Features for Local Explanations. 1139-1149 - Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang:
Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks. 1150-1160 - Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang:
Graph Adversarial Attack via Rewiring. 1161-1169 - Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua T. Vogelstein, Randal C. Burns:
BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment. 1170-1179 - Neil G. Marchant, Benjamin I. P. Rubinstein:
Needle in a Haystack: Label-Efficient Evaluation under Extreme Class Imbalance. 1180-1190 - Maxwell J. McNeil, Lin Zhang, Petko Bogdanov:
Temporal Graph Signal Decomposition. 1191-1201 - Chuizheng Meng, Sirisha Rambhatla, Yan Liu:
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. 1202-1211 - Mike A. Merrill, Ge Zhang, Tim Althoff:
MULTIVERSE: Mining Collective Data Science Knowledge from Code on the Web to Suggest Alternative Analysis Approaches. 1212-1222 - Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Susie Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang:
DeGNN: Improving Graph Neural Networks with Graph Decomposition. 1223-1233 - Anasua Mitra, Priyesh Vijayan, Sanasam Ranbir Singh, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran:
Semi-Supervised Deep Learning for Multiplex Networks. 1234-1244