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ORCID32nd WWW 2023: Austin, TX, USA
- Ying Ding, Jie Tang, Juan F. Sequeda, Lora Aroyo, Carlos Castillo, Geert-Jan Houben:
Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023. ACM 2023, ISBN 978-1-4503-9416-1
Keynotes
- Barbara Poblete
:
Using diversity as a source of scientific innovation for the Web. 1 - Payal Arora:
Decolonizing Creative Labor in the age of AI. 2 - Zachary Elkins:
Concept Regulation in the Social Sciences. 3 - Michalis Vazirgiannis:
GNNs and Graph Generative models for biomedical applications. 4 - Robert Melancton Metcalfe:
Connectivity. 5
Social Network Analysis and Graph Algorithms
- Masoud Reyhani Hamedani, Jin-Su Ryu, Sang-Wook Kim:
GELTOR: A Graph Embedding Method based on Listwise Learning to Rank. 6-16 - Lianghao Xia, Chao Huang, Jiao Shi, Yong Xu:
Graph-less Collaborative Filtering. 17-27 - Zheyuan Liu, Chunhui Zhang, Yijun Tian, Erchi Zhang, Chao Huang, Yanfang Ye, Chuxu Zhang:
Fair Graph Representation Learning via Diverse Mixture-of-Experts. 28-38 - Yuchen Zhou, Yanan Cao, Yongchao Liu, Yanmin Shang, Peng Zhang, Zheng Lin, Yun Yue, Baokun Wang, Xing Fu, Weiqiang Wang:
Multi-Aspect Heterogeneous Graph Augmentation. 39-48 - Florian Adriaens, Simon Apers:
Testing Cluster Properties of Signed Graphs. 49-59 - Zeyu Zhang, Jiamou Liu, Xianda Zheng, Yifei Wang, Pengqian Han, Yupan Wang, Kaiqi Zhao, Zijian Zhang:
RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks. 60-70 - Taehyung Kwon, Jihoon Ko, Jinhong Jung, Kijung Shin:
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors. 71-81 - Hao Huang, Keqi Han, Beicheng Xu, Ting Gan:
Multi-aspect Diffusion Network Inference. 82-90 - Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr:
Collaboration-Aware Graph Convolutional Network for Recommender Systems. 91-101 - Chao Gao, Yuchen Wang, Zhen Wang, Xianghua Li, Xuelong Li:
Pairwise-interactions-based Bayesian Inference of Network Structure from Information Cascades. 102-110 - Jiazheng Zhang, Bang Wang:
Encoding Node Diffusion Competence and Role Significance for Network Dismantling. 111-121 - Zhilun Zhou, Yu Liu, Jingtao Ding, Depeng Jin, Yong Li:
Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network. 122-132 - Xiaotian Zhou, Zhongzhi Zhang:
Opinion Maximization in Social Networks via Leader Selection. 133-142 - Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao:
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking. 143-153 - Dong Chen, Xiang Zhao, Wei Wang, Zhen Tan, Weidong Xiao:
Graph Self-supervised Learning with Augmentation-aware Contrastive Learning. 154-164 - Jiajie Su, Chaochao Chen, Weiming Liu, Fei Wu, Xiaolin Zheng, Haoming Lyu:
Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation. 165-176 - Liangtian Wan, Xiaona Li, Huijin Han, Xiaoran Yan, Lu Sun, Zhaolong Ning, Feng Xia:
Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters. 177-187 - Chenxu Wang, Xin Wang, Zhao Li, Zirui Chen, Jianxin Li:
HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks. 188-198 - Ke Zhang, Xiaoqing Wang, Gong Cheng:
Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem. 199-209 - Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier:
Neighborhood Structure Configuration Models. 210-220 - Yang Liu, Chuan Zhou, Shirui Pan, Jia Wu, Zhao Li, Hongyang Chen, Peng Zhang:
CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing. 221-230 - Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, Sang-Wook Kim:
Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding. 231-239 - Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, Philip S. Yu:
ConsRec: Learning Consensus Behind Interactions for Group Recommendation. 240-250 - Cheng Yang, Xumeng Gong, Chuan Shi, Philip S. Yu:
A Post-Training Framework for Improving Heterogeneous Graph Neural Networks. 251-262 - Trung-Kien Nguyen, Zemin Liu, Yuan Fang:
Link Prediction on Latent Heterogeneous Graphs. 263-273 - Wendong Bi, Bingbing Xu, Xiaoqian Sun, Easton Li Xu, Huawei Shen, Xueqi Cheng:
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network. 274-285 - Zhen Wang, Dongpeng Hou, Chao Gao, Xiaoyu Li, Xuelong Li:
Lightweight source localization for large-scale social networks. 286-294 - Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, Siu-Ming Yiu:
Automated Spatio-Temporal Graph Contrastive Learning. 295-305 - Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang:
Graph Neural Networks with Diverse Spectral Filtering. 306-316 - Hyunju Kim, Jihoon Ko, Fanchen Bu, Kijung Shin:
Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes. 317-327 - Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, Dongmei Zhang:
Robust Mid-Pass Filtering Graph Convolutional Networks. 328-338 - Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin:
Semi-decentralized Federated Ego Graph Learning for Recommendation. 339-348 - Xiran Song, Jianxun Lian, Hong Huang, Zihan Luo, Wei Zhou, Xue Lin, Mingqi Wu, Chaozhuo Li, Xing Xie, Hai Jin:
xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction. 349-359 - Junda Ye, Zhongbao Zhang, Li Sun, Yang Yan, Feiyang Wang, Fuxin Ren:
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds. 360-371 - Zhichen Zeng, Si Zhang, Yinglong Xia, Hanghang Tong:
PARROT: Position-Aware Regularized Optimal Transport for Network Alignment. 372-382 - Weiming Liu, Xiaolin Zheng, Chaochao Chen, Jiajie Su, Xinting Liao, Mengling Hu, Yanchao Tan:
Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation. 383-394 - Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang, Kun Kuang, Yan Zhang, Yulin Kang, Jun Zhou:
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling. 395-405 - Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, Junzhou Luo:
Label Information Enhanced Fraud Detection against Low Homophily in Graphs. 406-416 - Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang:
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. 417-428 - Guozhen Zhang, Tian Ye, Depeng Jin, Yong Li:
An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction. 429-437 - Bingxin Zhou, Yuanhong Jiang, Yuguang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, Xiaoqun Zhang:
Robust Graph Representation Learning for Local Corruption Recovery. 438-448 - Zhongxuan Han, Xiaolin Zheng, Chaochao Chen, Wenjie Cheng, Yang Yao:
Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation. 449-459 - Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan, Jia Wu, Hao Peng, Jianxin Li:
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification. 460-468 - Liang Yang, Qiuliang Zhang, Runjie Shi, Wenmiao Zhou, Bingxin Niu, Chuan Wang, Xiaochun Cao, Dongxiao He, Zhen Wang, Yuanfang Guo:
Graph Neural Networks without Propagation. 469-477 - Yao Zhang, Yun Xiong, Yongxiang Liao, Yiheng Sun, Yucheng Jin, Xuehao Zheng, Yangyong Zhu:
TIGER: Temporal Interaction Graph Embedding with Restarts. 478-488 - Liangtian Wan, Zhenqiang Fu, Lu Sun, Xianpeng Wang, Gang Xu, Xiaoran Yan, Feng Xia:
Self-Supervised Teaching and Learning of Representations on Graphs. 489-498 - Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, Jianxin Li, Jia Wu, Chunyang Liu, Philip S. Yu:
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization. 499-510 - Jiayan Guo, Lun Du, Wendong Bi, Qiang Fu, Xiaojun Ma, Xu Chen, Shi Han, Dongmei Zhang, Yan Zhang:
Homophily-oriented Heterogeneous Graph Rewiring. 511-522 - Qijie Bai, Changli Nie, Haiwei Zhang, Dongming Zhao, Xiaojie Yuan:
HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction. 523-532 - Xiaojun Ma, Qin Chen, Yi Wu, Guojie Song, Liang Wang, Bo Zheng:
Rethinking Structural Encodings: Adaptive Graph Transformer for Node Classification Task. 533-544 - Hsi-Wen Chen, De-Nian Yang, Wang-Chien Lee, Philip S. Yu, Ming-Syan Chen:
CMINet: a Graph Learning Framework for Content-aware Multi-channel Influence Diffusion. 545-555 - Han Xie, Li Xiong, Carl J. Yang:
Federated Node Classification over Graphs with Latent Link-type Heterogeneity. 556-566 - Susheel Suresh, Mayank Shrivastava, Arko Mukherjee, Jennifer Neville, Pan Li:
Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs. 567-577 - Ylli Sadikaj, Justus Rass, Yllka Velaj, Claudia Plant:
Semi-Supervised Embedding of Attributed Multiplex Networks. 578-587 - Lanning Wei, Zhiqiang He, Huan Zhao, Quanming Yao:
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification. 588-598 - Qiheng Mao, Zemin Liu, Chenghao Liu, Jianling Sun:
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer. 599-610 - Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan:
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs. 611-620 - Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Xiangyu Zhao, Guandong Xu:
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning. 621-629 - Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du:
Minimum Topology Attacks for Graph Neural Networks. 630-640 - Riting Xia, Yan Zhang, Chunxu Zhang, Xueyan Liu, Bo Yang:
Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks. 641-650 - Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He:
GIF: A General Graph Unlearning Strategy via Influence Function. 651-661 - Fabian Spaeh, Charalampos E. Tsourakakis:
Learning Mixtures of Markov Chains with Quality Guarantees. 662-672 - Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi, Chaochao Chen, Longbiao Chen:
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging. 673-683 - Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan:
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation. 684-693 - Nate Veldt:
Cut-matching Games for Generalized Hypergraph Ratio Cuts. 694-704 - Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang:
Toward Degree Bias in Embedding-Based Knowledge Graph Completion. 705-715 - Chao Pan, Eli Chien, Olgica Milenkovic:
Unlearning Graph Classifiers with Limited Data Resources. 716-726 - Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu:
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks. 727-736 - Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang:
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. 737-746 - Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang:
CogDL: A Comprehensive Library for Graph Deep Learning. 747-758 - Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, Jie Tang:
ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation. 759-769
User Modeling and Personalization
- Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee:
Enhancing User Personalization in Conversational Recommenders. 770-778 - Ruitao Zhu, Detao Lv, Yao Yu, Ruihao Zhu, Zhenzhe Zheng, Ke Bu, Quan Lu, Fan Wu:
LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation. 779-789 - Wei Wei, Chao Huang, Lianghao Xia, Chuxu Zhang:
Multi-Modal Self-Supervised Learning for Recommendation. 790-800 - SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu:
Distillation from Heterogeneous Models for Top-K Recommendation. 801-811 - Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He:
On the Theories Behind Hard Negative Sampling for Recommendation. 812-822 - Tianjun Wei, Jianghong Ma, Tommy W. S. Chow:
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation. 823-832 - Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Peng Jiang, Kun Gai, Xiangyu Zhao, Yongfeng Zhang:
Exploration and Regularization of the Latent Action Space in Recommendation. 833-844 - Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang:
Bootstrap Latent Representations for Multi-modal Recommendation. 845-854 - Yu Yin, Le Dai, Zhenya Huang, Shuanghong Shen, Fei Wang, Qi Liu, Enhong Chen, Xin Li:
Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer. 855-864 - Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai:
Two-Stage Constrained Actor-Critic for Short Video Recommendation. 865-875 - Jingsen Zhang, Xu Chen, Jiakai Tang, Weiqi Shao, Quanyu Dai, Zhenhua Dong, Rui Zhang:
Recommendation with Causality enhanced Natural Language Explanations. 876-886 - Chuang Zhao, Hongke Zhao, Ming He, Jian Zhang, Jianping Fan:
Cross-domain recommendation via user interest alignment. 887-896 - Zhenlei Wang, Xu Chen:
Robust Recommendation with Adversarial Gaussian Data Augmentation. 897-905 - Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, Yong Li:
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs. 906-916 - Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song, Zhiheng Li, Depeng Jin, Yong Li:
Dual-interest Factorization-heads Attention for Sequential Recommendation. 917-927 - Zhihui Zhou, Lilin Zhang, Ning Yang:
Contrastive Collaborative Filtering for Cold-Start Item Recommendation. 928-937 - Xiaoyu You, Chi Li, Daizong Ding, Mi Zhang, Fuli Feng, Xudong Pan, Min Yang:
Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model. 938-948 - Yue Cui, Chen Ma, Kai Zheng, Lei Chen, Xiaofang Zhou:
Controllable Universal Fair Representation Learning. 949-959 - Wei Guo, Chang Meng, Enming Yuan, Zhicheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang:
Compressed Interaction Graph based Framework for Multi-behavior Recommendation. 960-970 - Wenzhuo Song, Shoujin Wang, Yan Wang, Kunpeng Liu, Xueyan Liu, Minghao Yin:
A Counterfactual Collaborative Session-based Recommender System. 971-982 - Zixuan Xu, Penghui Wei, Shaoguo Liu, Weimin Zhang, Liang Wang, Bo Zheng:
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation. 983-991 - Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao:
Automated Self-Supervised Learning for Recommendation. 992-1002 - Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang:
AutoDenoise: Automatic Data Instance Denoising for Recommendations. 1003-1011 - Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang:
Improving Recommendation Fairness via Data Augmentation. 1012-1020 - Shiguang Wu, Yaqing Wang, Qinghe Jing, Daxiang Dong, Dejing Dou, Quanming Yao:
ColdNAS: Search to Modulate for User Cold-Start Recommendation. 1021-1031 - Rui Fan, Yuanhao Pu, Jin Chen, Zhihao Zhu, Defu Lian, Enhong Chen:
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation. 1032-1042 - Lingfeng Shi, Yuang Liu, Jun Wang, Wei Zhang:
Quantize Sequential Recommenders Without Private Data. 1043-1052 - Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin:
Interaction-level Membership Inference Attack Against Federated Recommender Systems. 1053-1062 - Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang, Da Luo, Kangyi Lin:
Debiased Contrastive Learning for Sequential Recommendation. 1063-1073 - Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-Yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou:
Clustered Embedding Learning for Recommender Systems. 1074-1084 - Jiawei Chen, Junkang Wu, Jiancan Wu, Xuezhi Cao, Sheng Zhou, Xiangnan He:
Adap-τ : Adaptively Modulating Embedding Magnitude for Recommendation. 1085-1096 - Yuhan Quan