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---
---
# Journal Articles
@article{han2025survey,
title = {A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications},
author = {Jiaqi Han and Jiacheng Cen and Liming Wu and Zongzhao Li and Xiangzhe Kong and Rui Jiao and Ziyang Yu and Tingyang Xu and Fandi Wu and Zihe Wang and Hongteng Xu and Zhewei Wei and Yang Liu* and Yu Rong* and Wenbing Huang*},
arxiv = {2403.00485},
journal = {Frontiers of Computer Science},
year = {2025},
abbr = {FCS},
toappear = {true}
}
@article{li2025survey,
title = {A Survey of Estimating Number of Distinct Values},
journal = {Frontiers of Computer Science},
author = {Li, Jiajun and Lei, Runlin and Wei*, Zhewei},
year = {2025},
abbr = {FCS},
toappear = {true}
}
@article{ding2025scalable,
title = {Scalable and Effective Graph Neural Networks via Trainable Random Walk Sampling},
journal = {IEEE Transactions on Knowledge and Data Engineering},
author = {Ding, Haipeng and Wei*, Zhewei and Ye, Yuhang},
year = {2025},
abbr = {TKDE},
html = {https://ieeexplore.ieee.org/document/10786281},
toappear = {true}
}
@article{kuang2024transformer,
title = {When Transformer Meets Large Graphs: An Expressive and Efficient Two-View Architecture},
ISSN = {2326-3865},
html = {http://dx.doi.org/10.1109/tkde.2024.3381125},
DOI = {10.1109/tkde.2024.3381125},
journal = {IEEE Transactions on Knowledge and Data Engineering},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Kuang, Weirui and Wang*, Zhen and Wei, Zhewei and Li, Yaliang and Ding, Bolin},
year = {2024},
pages = {1-13},
abbr = {TKDE},
html = {https://ieeexplore.ieee.org/document/10479175}
}
@article{zheng2024survey,
title = {A Survey of Dynamic Graph Neural Networks},
journal = {Frontiers of Computer Science},
author = {Zheng, Yanping and Yi, Lu and Wei*, Zhewei},
year = {2024},
abbr = {FCS},
html = {https://journal.hep.com.cn/fcs/EN/10.1007/s11704-024-3853-2},
arxiv = {2404.18211},
}
@article{wang2024survey,
title = {A Survey on Large Language Model Based Autonomous Agents},
volume = {18},
ISSN = {2095-2236},
html = {http://dx.doi.org/10.1007/s11704-024-40231-1},
DOI = {10.1007/s11704-024-40231-1},
number = {6},
journal = {Frontiers of Computer Science},
publisher = {Springer Science and Business Media LLC},
author = {Wang, Lei and Ma, Chen and Feng, Xueyang and Zhang, Zeyu and Yang, Hao and Zhang, Jingsen and Chen, Zhiyuan and Tang, Jiakai and Chen*, Xu and Lin*, Yankai and Zhao, Wayne Xin and Wei, Zhewei and Wen, Ji-Rong},
year = {2024},
month = {mar},
abbr = {FCS},
html = {https://link.springer.com/article/10.1007/s11704-024-40231-1},
arxiv = {2308.11432}
}
@article{yang2024efficient,
title = {Efficient Algorithms for Personalized PageRank Computation: A Survey},
journal = {IEEE Transactions on Knowledge and Data Engineering},
author = {Yang, Mingji and Wang, Hanzhi and Wei*, Zhewei and Wang, Sibo and Wen, Ji-Rong},
year = {2024},
abbr = {TKDE},
field = {Graph Algorithms},
html = {https://doi.org/10.1109/TKDE.2024.3376000},
arxiv = {2403.05198},
selected = {true},
abstract = {In this survey paper, we give a systematic and comprehensive review of several basic techniques and recent algorithms for PPR computation from an algorithmic perspective.}
}
@article{yuan2024indexfree,
title = {Index-Free Triangle-Based Graph Local Clustering},
journal = {Frontiers of Computer Science},
author = {Yuan, Zhe and Wei*, Zhewei and Lv, Fangrui and Wen, Ji-Rong},
year = {2024},
abbr = {FCS},
html = {https://link.springer.com/article/10.1007/s11704-023-2768-7}
}
@article{hu2023enabling,
title = {Enabling Efficient Random Access to Hierarchically Compressed Text Data on Diverse GPU Platforms},
volume = {34},
ISSN = {2161-9883},
html = {http://dx.doi.org/10.1109/tpds.2023.3294341},
DOI = {10.1109/tpds.2023.3294341},
number = {10},
journal = {IEEE Transactions on Parallel and Distributed Systems},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Hu, Yihua and Zhang*, Feng and Xia, Yifei and Yao, Zhiming and Zeng, Letian and Ding, Haipeng and Wei, Zhewei and Zhang, Xiao and Zhai, Jidong and Du, Xiaoyong and Ma, Siqi},
year = {2023},
month = {oct},
pages = {2699-2717},
html = {https://ieeexplore.ieee.org/document/10178044},
abbr = {TPDS}
}
@article{guo2022influence,
title = {Influence Maximization Revisited: Efficient Sampling with Bound Tightened},
volume = {47},
ISSN = {1557-4644},
html = {http://dx.doi.org/10.1145/3533817},
DOI = {10.1145/3533817},
number = {3},
journal = {ACM Transactions on Database Systems},
publisher = {Association for Computing Machinery (ACM)},
author = {Guo, Qintian and Wang*, Sibo and Wei*, Zhewei and Lin, Wenqing and Tang, Jing},
year = {2022},
month = {aug},
pages = {1-45},
html = {https://dl.acm.org/doi/pdf/10.1145/3533817},
abbr = {TODS}
}
@article{zeng2022persistent,
title = {Persistent Summaries},
volume = {47},
ISSN = {1557-4644},
html = {http://dx.doi.org/10.1145/3531053},
DOI = {10.1145/3531053},
number = {3},
journal = {ACM Transactions on Database Systems},
publisher = {Association for Computing Machinery (ACM)},
author = {Zeng, Tianjing and Wei*, Zhewei and Luo, Ge and Yi, Ke and Du, Xiaoyong and Wen, Ji-Rong},
year = {2022},
month = {aug},
pages = {1-42},
html = {https://dl.acm.org/doi/10.1145/3531053},
abbr = {TODS},
selected = {true},
field = {Streaming Algorithms},
abstract = {We propose the persistent summaries for streaming algorithms, enabling them to answer queries about the streaming data at any prior time.}
}
@article{liu2022building,
title = {Building Graphs at Scale via Sequence of Edges: Model and Generation Algorithms},
journal = {IEEE Transactions on Knowledge and Data Engineering},
author = {Liu, Yu and Zou*, Lei and Wei, Zhewei},
year = {2022},
abbr = {TKDE},
html = {https://ieeexplore.ieee.org/document/9435056/},
code = {https://github.com/pkumod/TemporalPreferentialAttachment}
}
@article{wang2021exactsim,
title = {ExactSim: Benchmarking Single-Source SimRank Algorithms with High-Precision Ground Truths},
journal = {The VLDB Journal},
author = {Wang, Hanzhi and Wei, Zhewei and Liu, Yu and Yuan, Ye and Du, Xiaoyong and Wen, Ji-Rong},
year = {2021},
field = {Graph Algorithms},
html = {https://link.springer.com/article/10.1007/s00778-021-00672-7},
code = {https://github.com/wanghzccls/ExactSim}
}
@article{yang2020gamebased,
title = {A Game-Based Framework for Crowdsourced Data Labeling},
volume = {29},
ISSN = {0949-877X},
html = {http://dx.doi.org/10.1007/s00778-020-00613-w},
DOI = {10.1007/s00778-020-00613-w},
number = {6},
journal = {The VLDB Journal},
publisher = {Springer Science and Business Media LLC},
author = {Yang, Jingru and Fan*, Ju and Wei, Zhewei and Li, Guoliang and Liu, Tongyu and Du, Xiaoyong},
year = {2020},
month = {may},
pages = {1311-1336},
html = {https://link.springer.com/article/10.1007/s00778-020-00613-w}
}
@article{wang2019efficient,
title = {Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries},
journal = {ACM Transactions on Database Systems},
author = {Wang, Sibo and Yang, Renchi and Wang, Runhui and Xiao, Xiaokui and Wei*, Zhewei and Lin, Wenqing and Yang, Yin and Tang, Nan},
year = {2019},
abbr = {TODS},
field = {Graph Algorithms},
html = {https://dl.acm.org/doi/10.1145/3360902},
arxiv = {1908.10583},
code = {https://github.com/wangsibovictor/fora}
}
@article{shang2019parallel,
title = {Parallel Trajectory-to-Location Join},
journal = {IEEE Transactions on Knowledge and Data Engineering},
author = {Shang, Shuo and Chen, Lisi and Zheng*, Kai and Jensen, Christian S. and Wei, Zhewei and Kalnis, Panos},
year = {2019},
abbr = {TKDE},
html = {https://ieeexplore.ieee.org/document/8409287}
}
@article{fan2019distributionaware,
title = {Distribution-Aware Crowdsourced Entity Collection},
volume = {31},
ISSN = {2326-3865},
html = {http://dx.doi.org/10.1109/tkde.2016.2611509},
DOI = {10.1109/tkde.2016.2611509},
number = {7},
journal = {IEEE Transactions on Knowledge and Data Engineering},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Fan, Ju and Wei, Zhewei and Zhang, Dongxiang and Yang, Jingru and Du, Xiaoyong},
year = {2019},
month = {jul},
pages = {1312–1326},
html = {https://ieeexplore.ieee.org/document/7572151},
abbr = {TKDE}
}
@article{wei2018tight,
title = {Tight Space Bounds for Two-Dimensional Approximate Range Counting},
volume = {14},
ISSN = {1549-6333},
html = {http://dx.doi.org/10.1145/3205454},
DOI = {10.1145/3205454},
number = {2},
journal = {ACM Transactions on Algorithms},
publisher = {Association for Computing Machinery (ACM)},
author = {Wei, Zhewei and Yi, Ke},
year = {2018},
month = {apr},
pages = {1–17},
html = {https://doi.org/10.1145/3205454},
abbr = {TALG},
selected = {true},
field = {Data Structures},
abstract = {We study the problem of two-dimensional orthogonal range counting with additive error and give tight space bounds.}
}
@article{shang2018parallel,
title = {Parallel Trajectory Similarity Joins in Spatial Networks},
volume = {27},
ISSN = {0949-877X},
html = {http://dx.doi.org/10.1007/s00778-018-0502-0},
DOI = {10.1007/s00778-018-0502-0},
number = {3},
journal = {The VLDB Journal},
publisher = {Springer Science and Business Media LLC},
author = {Shang, Shuo and Chen*, Lisi and Wei, Zhewei and Jensen, Christian S. and Zheng, Kai and Kalnis, Panos},
year = {2018},
month = {apr},
pages = {395–420},
html = {https://link.springer.com/article/10.1007/s00778-018-0502-0}
}
@article{lin2017optimal,
title = {Optimal Algorithms for Selecting Top-k Combinations of Attributes: Theory and Applications},
volume = {27},
ISSN = {0949-877X},
html = {http://dx.doi.org/10.1007/s00778-017-0485-2},
DOI = {10.1007/s00778-017-0485-2},
number = {1},
journal = {The VLDB Journal},
publisher = {Springer Science and Business Media LLC},
author = {Lin, Chunbin and Lu, Jiaheng and Wei, Zhewei and Wang, Jianguo and Xiao, Xiaokui},
year = {2017},
month = {oct},
pages = {27–52},
html = {https://link.springer.com/article/10.1007/s00778-017-0485-2}
}
@article{shang2016dynamic,
title = {Dynamic Shortest Path Monitoring in Spatial Networks},
volume = {31},
ISSN = {1860-4749},
html = {http://dx.doi.org/10.1007/s11390-016-1653-3},
DOI = {10.1007/s11390-016-1653-3},
number = {4},
journal = {Journal of Computer Science and Technology},
publisher = {Springer Science and Business Media LLC},
author = {Shang, Shuo and Chen, Lisi and Wei, Zhewei and Guo*, Danhuai and Wen, Ji-Rong},
year = {2016},
month = {jul},
pages = {637–648},
html = {https://link.springer.com/article/10.1007/s11390-016-1653-3},
abbr = {JCST}
}
@article{shang2015collective,
title = {Collective Travel Planning in Spatial Networks},
author = {Shang, Shuo and Chen, Lisi and Wei, Zhewei and Jensen, Christian S and Wen, Ji-Rong and Kalnis, Panos},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {28},
number = {5},
pages = {1132--1146},
year = {2015},
publisher = {IEEE},
html = {https://ieeexplore.ieee.org/document/7360162},
abbr = {TKDE}
}
@article{yi2014indexing,
title = {Indexing for Summary Queries: Theory and Practice},
html = {http://dx.doi.org/10.1145/2508702},
journal = {ACM Transactions on Database Systems},
author = {Yi, Ke and Wang, Lu and Wei, Zhewei},
year = {2014},
abbr = {TODS},
selected = {true},
field = {Data Structures},
abstract = {We design efficient indexing techniques that enable extracting statistical summaries, such as frequent items, quantiles, and sketches with linear space requirements and optimal or near-optimal query cost.}
}
@article{pagh2013cacheoblivious,
title = {**Cache-Oblivious Hashing},
volume = {69},
ISSN = {1432-0541},
html = {http://dx.doi.org/10.1007/s00453-013-9763-6},
DOI = {10.1007/s00453-013-9763-6},
number = {4},
journal = {Algorithmica},
publisher = {Springer Science and Business Media LLC},
author = {Pagh, Rasmus and Wei, Zhewei and Yi, Ke and Zhang, Qin},
year = {2013},
month = {mar},
pages = {864–883},
}
@article{agarwal2013mergeable,
title = {**Mergeable Summaries},
author = {Agarwal, Pankaj K and Cormode, Graham and Huang, Zengfeng and Phillips, Jeff M and Wei, Zhewei and Yi*, Ke},
journal = {ACM Transactions on Database Systems},
volume = {38},
number = {4},
pages = {1--28},
year = {2013},
publisher = {ACM New York, NY, USA},
html = {https://dl.acm.org/doi/10.1145/2500128},
abbr = {TODS}
}
# Conference Articles
@inproceedings{cui2025mixing,
title = {Mixing Time Matters: Accelerating Effective Resistance Estimation via Bidirectional Method},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Cui, Guanyu and Wang, Hanzhi and Wei*, Zhewei},
year = {2025},
abbr = {KDD},
field = {Graph Algorithms},
code = {https://github.com/GuanyuCui/BiSPER},
toappear = {true}
}
@inproceedings{ding2025largescale,
title = {Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Ding, Haipeng and Wei*, Zhewei and Ye, Yuhang},
year = {2025},
abbr = {KDD},
arxiv = {2501.04570},
toappear = {true}
}
@inproceedings{zeng2025price,
title = {PRICE: A Pretrained Model for Cross-Database Cardinality Estimation},
booktitle = {International Conference on Very Large Data Bases},
author = {Zeng, Tianjing and Lan, Junwei and Ma, Jiahong and Wei, Wenqing and Zhu*, Rong and Li, Pengfei and Ding*, Bolin and Lian, Defu and Wei, Zhewei and Zhou*, Jingren},
year = {2025},
abbr = {VLDB},
arxiv = {2406.01027},
code = {https://github.com/StCarmen/PRICE},
toappear = {true}
}
@inproceedings{lei2024intruding,
title = {Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level},
booktitle = {Annual Conference on Neural Information Processing Systems},
author = {Lei, Runlin and Hu, Yuwei and Ren, Yuchen and Wei*, Zhewei},
year = {2024},
abbr = {NeurIPS},
html = {https://openreview.net/forum?id=oTzydUKWpq},
arxiv = {2405.16405},
code = {https://github.com/Leirunlin/Text-level-Graph-Attack}
}
@inproceedings{zhou2024smolsearch,
title = {S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search},
booktitle = {Annual Conference on Neural Information Processing Systems},
author = {Zhou, Gengmo and Wang, Zhen and Yu, Feng and Ke, Guolin and Wei*, Zhewei and Gao*, Zhifeng},
year = {2024},
abbr = {NeurIPS},
html = {https://openreview.net/forum?id=wJAF8TGVUG},
arxiv = {2409.07462},
code ={https://bohrium.dp.tech/apps/s-molsearch}
}
@inproceedings{ji2024srapagent,
title = {SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-Based Agent},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
author = {Ji, Jiarui and Li, Yang and Liu, Hongtao and Du, Zhicheng and Wei, Zhewei and Qi, Qi and Shen, Weiran and Lin*, Yankai},
year = {2024},
abbr = {EMNLP Findings},
arxiv = {2410.14152},
html = {https://aclanthology.org/2024.findings-emnlp.15/},
code = {https://github.com/jijiarui-cather/SRAPAgent_Framework}
}
@inproceedings{peng2024oversmoothing,
title = {Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks},
author = {Peng, Jie and Lei, Runlin and Wei* Zhewei},
booktitle = {ACM International Conference on Information and Knowledge Management},
abbr = {CIKM},
year = {2024},
arxiv = {2408.03669},
html = {https://dl.acm.org/doi/10.1145/3627673.3679776}
}
@inproceedings{feng2024federated,
title = {Federated Heterogeneous Contrastive Distillation for Molecular Representation Learning},
author = {Feng, Jinjia and Wang, Zhen and Wei*, Zhewei and Li, Yaliang and Ding, Bolin and Xu, Hongteng},
booktitle = {ACM International Conference on Information and Knowledge Management},
abbr = {CIKM},
year = {2024},
html = {https://dl.acm.org/doi/10.1145/3627673.3679725}
}
@inproceedings{ma2024polyformer,
title = {PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
html = {http://dx.doi.org/10.1145/3637528.3671849},
year = {2024},
abbr = {KDD},
author = {Ma, Jiahong and He, Mingguo and Wei*, Zhewei},
arxiv = {2407.14459},
code = {https://github.com/air029/PolyFormer}
}
@inproceedings{zhang2024equipocket,
title = {EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction},
author = {Zhang, Yang and Wei*, Zhewei and Yuan, Ye and Li, Chongxuan and Huang*, Wenbing},
booktitle = {International Conference on Machine Learning},
award = {Oral},
year = {2024},
arxiv = {2302.12177},
abbr = {ICML},
selected = {true},
field = {AI for Science},
html = {https://openreview.net/forum?id=1vGN3CSxVs},
code = {https://github.com/fengyuewuya/EquiPocket},
abstract = {We propose EquiPocket, the first equivariant GNN-based method for ligand binding site prediction.}
}
@inproceedings{yin2024optimal,
title = {Optimal Matrix Sketching over Sliding Windows},
booktitle = {International Conference on Very Large Data Bases},
author = {Yin, Hanyan and Wen, Dongxie and Li, Jiajun and Wei*, Zhewei and Zhang, Xiao and Huang, Zengfeng and Li, Feifei},
year = {2024},
abbr = {VLDB},
field = {Streaming Algorithms},
html = {https://dl.acm.org/doi/10.14778/3665844.3665847},
arxiv = {2405.07792},
code = {https://github.com/yinhanyan/DS-FD},
poster = {yin2024optimal_poster.pdf},
slides = {yin2024optimal_slides.pptx},
award = {Best Paper Nomination},
selected = {true},
abstract = {We introduce the DS-FD algorithm, which achieves the optimal \(O\left(d/\varepsilon\right)\)~space bound to maintain a sketch \(\boldsymbol\{B\}\in\mathbb\{R\}^\{O\left(\{1\over\varepsilon\}\right)\times d\}\)~of a matrix \(\boldsymbol\{A\}\in\mathbb\{R\}^\{W \times d\}\)~consisting of the most recent \(d\)-dimension rows of a data stream in the row-update, \(W\)-sized sliding window model, with a covariance error bound of \(\lVert \boldsymbol\{A\}^\top\boldsymbol\{A\}-\boldsymbol\{B\}^\top\boldsymbol\{B\}\rVert_2\le \varepsilon \lVert \boldsymbol\{A\}\rVert_F^2\). },
}
@inproceedings{zhang2024predicting,
title = {HierAffinity: Predicting Protein-Ligand Binding Affinity With Hierarchical Modeling},
abbr = {DASFAA},
booktitle = {International Conference on Database Systems for Advanced Applications},
author = {Zhang, Yang and Wei*, Zhewei and Huang*, Wenbing and Li, Chongxuan},
year = {2024},
html = {https://doi.org/10.1007/978-981-97-5575-2_3}
}
@inproceedings{zhang2024transpocket,
title = {TransPocket: Structural and Geometric Transformer for Ligand Binding Site Detection},
booktitle = {International Conference on Database Systems for Advanced Applications},
author = {Zhang, Yang and Wei*, Zhewei and Huang*, Wenbing and Li, Chongxuan},
year = {2024},
abbr = {DASFAA},
html = {https://link.springer.com/chapter/10.1007/978-981-97-5575-2_12}
}
@inproceedings{li2024learning,
title = {Learning-based Property Estimation with Polynomials},
booktitle = {ACM Conference on Management of Data},
author = {Li, Jiajun and Lei, Runlin and Wang, Sibo and Wei*, Zhewei and Ding, Bolin},
year = {2024},
abbr = {SIGMOD},
html = {https://dl.acm.org/doi/abs/10.1145/3654994},
code = {https://github.com/llijiajun/Learning-based_Property_Estimation_with_Polynomials}
}
@inproceedings{wang2024revisiting,
title = {**Revisiting Local Computation of PageRank: Simple and Optimal},
booktitle = {Annual ACM Symposium on Theory of Computing},
author = {Wang, Hanzhi and Wei*, Zhewei and Wen, Ji-Rong and Yang, Mingji},
year = {2024},
abbr = {STOC},
field = {Graph Algorithms},
html = {https://dl.acm.org/doi/10.1145/3618260.3649661},
arxiv = {2403.12648},
video = {https://www.youtube.com/watch?v=ipWgICjGfRU},
slides = {STOC24.pptx},
selected = {true},
abstract = {We use simple techniques and analyses to give matching upper and lower bounds for estimating PageRank contributions and single-node PageRank. Our results for the upper bounds are derived by revisiting the known algorithms of ApproxContributions (a.k.a. Backward Push) and BiPPR.}
}
@inproceedings{wang2024exploring,
title = {Exploring Neural Scaling Law and Data Pruning Methods For Node Classification on Large-scale Graphs},
series = {WWW ’24},
award = {Oral},
volume = {201},
DOI = {10.1145/3589334.3645571},
booktitle = {The Web Conference},
publisher = {ACM},
author = {Wang, Zhen and Li, Yaliang and Ding, Bolin and Li, Yule and Wei, Zhewei},
year = {2024},
month = {may},
pages = {780–791},
collection = {TheWebConf},
abbr = {WWW},
html = {https://dl.acm.org/doi/10.1145/3589334.3645571},
code = {https://github.com/joneswong/GNSL}
}
@inproceedings{he2024spectral,
title = {Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials},
booktitle = {The Web Conference},
author = {He, Mingguo and Wei*, Zhewei and Feng, Shikun and Huang, Zhengjie and Li, Weibin and Sun, Yu and Yu, Dianhai},
year = {2024},
abbr = {WWW},
award = {Oral},
html = {https://dl.acm.org/doi/10.1145/3589334.3645515},
arxiv = {2305.19872},
code = {https://github.com/ivam-he/PSHGCN}
}
@inproceedings{chen2024polygcl,
title = {PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters},
author = {Chen, Jingyu and Lei, Runlin and Wei*, Zhewei},
booktitle = {International Conference on Learning Representations},
year = {2024},
html = {https://openreview.net/forum?id=y21ZO6M86t},
abbr = {ICLR},
award = {Spotlight},
selected = {true},
field = {Graph Learning},
abstract = {We propose POLYGCL, a GCL pipeline that utilizes polynomial filters to achieve contrastive learning between the low-pass and highpass views.},
code = {https://github.com/ChenJY-Count/PolyGCL}
}
@inproceedings{wei2024approximating,
title = {**Approximating Single-Source Personalized PageRank with Absolute Error Guarantees},
booktitle = {International Conference on Database Theory},
author = {Wei*, Zhewei and Wen, Ji-Rong and Yang, Mingji},
year = {2024},
abbr = {ICDT},
field = {Graph Algorithms},
html = {https://doi.org/10.4230/LIPIcs.ICDT.2024.9},
arxiv = {2401.01019}
}
@inproceedings{zhou2023deep,
title = {Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?},
booktitle = {International Conference on Learning Representations},
author = {Zhou, Gengmo and Gao, Zhifeng and Wei, Zhewei and Zheng, Hang and Ke*, Guolin},
year = {2023},
abbr = {ICLR},
award = {MLDD Oral},
html = {https://openreview.net/forum?id=W-Ikct539G},
arxiv = {2302.07061},
code = {https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c}
}
@inproceedings{wang2023estimating,
title = {Estimating Single-Node PageRank in \(\tilde\{O\}\left(\min\\{d_t, \sqrt\{m\}\\}\right)\) Time},
booktitle = {International Conference on Very Large Data Bases},
author = {Wang, Hanzhi and Wei*, Zhewei},
year = {2023},
abbr = {VLDB},
field = {Graph Algorithms},
html = {https://dl.acm.org/doi/10.14778/3611479.3611500},
arxiv = {2307.13162},
code = {https://github.com/wanghzccls/SetPush-code}
}
@inproceedings{cui2023MGNN,
title = {MGNN: Graph Neural Networks Inspired by Distance Geometry Problem},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Cui, Guanyu and Wei*, Zhewei},
year = {2023},
abbr = {KDD},
html = {https://dl.acm.org/doi/10.1145/3580305.3599431},
arxiv = {2201.12994},
code = {https://github.com/GuanyuCui/MGNN},
poster = {cui2023MGNN_poster.pdf},
slides = {cui2023MGNN_slides.pdf}
}
@inproceedings{yi2023optimal,
title = {Optimal Dynamic Subset Sampling: Theory and Applications},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Yi, Lu and Wang, Hanzhi and Wei*, Zhewei},
year = {2023},
abbr = {KDD},
html = {https://dl.acm.org/doi/10.1145/3580305.3599458},
arxiv = {2305.18785},
poster = {yi2023optimal_poster.pdf},
slides = {yi2023optimal_slides.pptx}
}
@inproceedings{guo2023clenshaw,
title = {Clenshaw Graph Neural Networks},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Guo, Yuhe and Wei*, Zhewei},
year = {2023},
abbr = {KDD},
html = {https://dl.acm.org/doi/10.1145/3580305.3599275},
arxiv = {2210.16508},
code = {https://github.com/yuziGuo/ClenshawGNN}
}
@inproceedings{guo2023graph,
title = {Graph Neural Networks with Learnable and Optimal Polynomial Bases},
booktitle = {International Conference on Machine Learning},
author = {Guo, Yuhe and Wei*, Zhewei},
year = {2023},
abbr = {ICML},
html = {https://proceedings.mlr.press/v202/guo23i.html},
arxiv = {2302.12432},
code = {https://github.com/yuziGuo/FarOptBasis}
}
@inproceedings{afshani2023range,
title = {**On Range Summary Queries},
author = {Afshani, Peyman and Cheng, Pingan and Roy, Aniket Basu and Wei, Zhewei},
booktitle = {International Colloquium on Automata, Languages and Programming},
year = {2023},
html = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.7},
arxiv = {2305.03180},
abbr = {ICALP}
}
@inproceedings{zheng2023decoupled,
title = {Decoupled Graph Neural Networks for Large Dynamic Graphs},
booktitle = {International Conference on Very Large Data Bases},
author = {Zheng, Yanping and Wei*, Zhewei and Liu, Jiajun},
year = {2023},
abbr = {VLDB},
html = {https://dl.acm.org/doi/10.14778/3598581.3598595},
arxiv = {2305.08273},
code = {https://github.com/zheng-yp/DecoupledDGNN}
}
@inproceedings{zhou2023unimol,
title = {Uni-Mol: A Universal 3D Molecular Representation Learning Framework},
author = {Zhou, Gengmo and Gao*, Zhifeng and Ding, Qiankun and Zheng, Hang and Xu, Hongteng and Wei, Zhewei and Zhang, Linfeng and Ke*, Guolin},
booktitle = {International Conference on Learning Representations},
year = {2023},
html = {https://openreview.net/forum?id=6K2RM6wVqKu},
abbr = {ICLR},
field = {AI for Science},
chemrxiv = {https://chemrxiv.org/engage/chemrxiv/article-details/6402990d37e01856dc1d1581},
code = {https://github.com/deepmodeling/Uni-Mol},
selected = {true},
abstract = {Uni-Mol is the first pure 3D molecular pretraining framework that can predict 3D positions, and the first molecular pretraining framework that can be directly used in 3D tasks in the field of drug design.}
}
@inproceedings{hou2023personalized,
title = {Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme},
booktitle = {ACM Conference on Management of Data},
author = {Hou, Guanhao and Guo, Qintian and Zhang, Fangyuan and Wang*, Sibo and Wei, Zhewei},
year = {2023},
abbr = {SIGMOD},
html = {https://dl.acm.org/doi/10.1145/3588705},
arxiv = {2212.10288}
}
@inproceedings{du2023preformer,
title = {Preformer: Predictive Transformer with Multi-Scale Segment-Wise Correlations for Long-Term Time Series Forecasting},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing},
author = {Du, Dazhao and Su*, Bing and Wei, Zhewei},
year = {2023},
abbr = {ICASSP},
html = {https://ieeexplore.ieee.org/document/10096881},
arxiv = {2202.11356},
code = {https://github.com/ddz16/Preformer}
}
@inproceedings{lei2022evennet,
title = {Evennet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks},
html = {https://dl.acm.org/doi/10.5555/3600270.3600609},
author = {Lei, Runlin and Wang, Zhen and Li, Yaliang and Ding, Bolin and Wei*, Zhewei},
booktitle = {Annual Conference on Neural Information Processing Systems},
volume = {35},
pages = {4694--4706},
year = {2022},
arxiv = {2205.13892},
code = {https://github.com/Leirunlin/EvenNet},
abbr = {NeurIPS}
}
@inproceedings{he2022convolutional,
title = {Convolutional Neural Networks On Graphs With Chebyshev Approximation, Revisited},
html = {https://dl.acm.org/doi/10.5555/3600270.3600797},
author = {He, Mingguo and Wei*, Zhewei and Wen, Ji-Rong},
booktitle = {Annual Conference on Neural Information Processing Systems},
award = {Oral},
volume = {35},
pages = {7264--7276},
year = {2022},
arxiv = {2202.03580},
code = {https://github.com/ivam-he/ChebNetII},
abbr = {NeurIPS},
selected = {true},
field = {Graph Learning},
abstract = {We revisit the problem of approximating the spectral graph convolutions with Chebyshev polynomials and then propose ChebNetII, a new GNN model based on Chebyshev interpolation, which enhances the original Chebyshev polynomial approximation while reducing the Runge phenomenon.}
}
@inproceedings{yang2022approximating,
title = {Approximating Probabilistic Group Steiner Trees in Graphs},
booktitle = {International Conference on Very Large Data Bases},
author = {Yang, Shuang and Sun*, Yahui and Liu, Jiesong and Xiao, Xiaokui and Li, Rong-Hua and Wei, Zhewei},
year = {2022},
abbr = {VLDB},
html = {http://dx.doi.org/10.14778/3565816.3565834},
code = {https://github.com/rucdatascience/PGST}
}
@inproceedings{feng2022mgmae,
title = {MGMAE: Molecular Representation Learning by Reconstructing Heterogeneous Graphs with A High Mask Ratio},
author = {Feng, Jinjia and Wang, Zhen and Li, Yaliang and Ding, Bolin and Wei*, Zhewei and Xu, Hongteng},
booktitle = {ACM International Conference on Information and Knowledge Management},
pages = {509--519},
year = {2022},
html = {https://dl.acm.org/doi/abs/10.1145/3511808.3557395},
abbr = {CIKM}
}
@inproceedings{zhang2022optimizing,
title = {Optimizing Random Access to Hierarchically-Compressed Data on GPU},
html = {http://dx.doi.org/10.1109/sc41404.2022.00023},
DOI = {10.1109/sc41404.2022.00023},
booktitle = {International Conference for High Performance Computing, Networking, Storage, and Analysis},
publisher = {IEEE},
author = {Zhang, Feng and Hu, Yihua and Ding, Haipeng and Yao, Zhiming and Wei, Zhewei and Zhang*, Xiao and Du, Xiaoyong},
year = {2022},
month = {nov},
html = {https://www.computer.org/csdl/proceedings-article/sc/2022/544400a233/1I0bSQibWEw},
abbr = {SC}
}
@inproceedings{li2022samplingbased,
title = {Sampling-based Estimation of the Number of Distinct Values in Distributed Environment},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Li, Jiajun and Wei*, Zhewei and Ding, Bolin and Dai, Xiening and Lu, Lu and Zhou, Jingren},
year = {2022},
abbr = {KDD},
html = {https://dl.acm.org/doi/10.1145/3534678.3539390},
arxiv = {2206.05476},
code = {https://github.com/llijiajun/NDV_Estimation_in_distributed_environment}
}
@inproceedings{wang2022graph,
title = {Graph Neural Networks with Node-wise Architecture},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
html = {http://dx.doi.org/10.1145/3534678.3539387},
author = {Wang, Zhen and Wei, Zhewei and Li*, Yaliang and Kuang, Weirui and Ding, Bolin},
year = {2022},
html = {https://dl.acm.org/doi/10.1145/3534678.3539387},
abbr = {KDD}
}
@inproceedings{zheng2022instant,
title = {Instant Graph Neural Networks for Dynamic Graphs},
series = {KDD ’22},
volume = {21},
html = {http://dx.doi.org/10.1145/3534678.3539352},
DOI = {10.1145/3534678.3539352},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
publisher = {ACM},
author = {Zheng, Yanping and Wang, Hanzhi and Wei*, Zhewei and Liu, Jiajun and Wang, Sibo},
year = {2022},
month = {aug},
pages = {2605–2615},
collection = {KDD ’22},
arxiv = {2206.01379},
abbr = {KDD},
code = {https://github.com/zheng-yp/InstantGNN}
}
@inproceedings{wang2022edgebased,
title = {Edge-based Local Push for Personalized PageRank},
booktitle = {International Conference on Very Large Data Bases},
author = {Wang, Hanzhi and Wei*, Zhewei and Gan, Junhao and Yuan, Ye and Du, Xiaoyong and Wen, Ji-Rong},
year = {2022},
abbr = {VLDB},
field = {Graph Algorithms},
html = {https://dl.acm.org/doi/10.14778/3523210.3523216},
arxiv = {2203.07937},
code = {https://github.com/wanghzccls/EdgePush}
}
@inproceedings{liu2022building,
title = {Building Graphs at Scale via Sequence of Edges: Model and Generation Algorithms},
booktitle = {IEEE International Conference on Data Engineering},
author = {Liu, Yu and Zou, Lei and Wei, Zhewei},
year = {2022},
abbr = {ICDE},
html = {https://ieeexplore.ieee.org/document/9835495}
}
@inproceedings{zhang2022predicting,
title = {Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling},
booktitle = {IEEE International Conference on Data Mining},
author = {Zhang, Yang and Zhou, Gengmo and Wei, Zhewei and Xu, Hongteng},
year = {2022},
abbr = {ICDM},
html = {https://ieeexplore.ieee.org/document/10027686},
arxiv = {2209.13014}
}
@inproceedings{wu2021learning,
title = {Learning to be a Statistician: Learned Estimator for Number of Distinct Values},
volume = {15},
ISSN = {2150-8097},
html = {http://dx.doi.org/10.14778/3489496.3489508},
DOI = {10.14778/3489496.3489508},
number = {2},
booktitle = {International Conference on Very Large Data Bases},
publisher = {Association for Computing Machinery (ACM)},
author = {Wu, Renzhi and Ding, Bolin and Chu, Xu and Wei, Zhewei and Dai, Xiening and Guan, Tao and Zhou, Jingren},
year = {2021},
month = {oct},
pages = {272–284},
arxiv = {2202.02800},
abbr = {VLDB},
code = {https://github.com/wurenzhi/learned_ndv_estimator}
}
@inproceedings{he2021bernnet,
title = {BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation},
author = {He, Mingguo and Wei*, Zhewei and Huang, Zengfeng and Xu*, Hongteng},
booktitle = {Annual Conference on Neural Information Processing Systems},
volume = {34},
pages = {14239--14251},
year = {2021},
field = {Graph Learning},
arxiv = {2106.10994},
html = {https://proceedings.neurips.cc/paper/2021/hash/76f1cfd7754a6e4fc3281bcccb3d0902-Abstract.html},
code = {https://github.com/ivam-he/BernNet},
abbr = {NeurIPS},
selected = {true},
abstract = {We propose the BernNet, a graph neural network that provides a simple and intuitive mechanism for designing and learning an arbitrary spectral filter via Bernstein polynomial approximation.}
}
@inproceedings{wang2021approximate,
title = {Approximate Graph Propagation},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
author = {Wang, Hanzhi and He, Mingguo and Wei*, Zhewei and Wang, Sibo and Yuan, Ye and Du, Xiaoyong and Wen, Ji-Rong},
year = {2021},
abbr = {KDD},
field = {Graph Algorithms},
html = {https://dl.acm.org/doi/10.1145/3447548.3467243},
arxiv = {2106.03058},
code = {https://github.com/wanghzccls/AGP-Approximate_Graph_Propagation},
slides = {https://weizhewei.com/slides/AGP_KDD_21_pre_final.pptx}
}
@inproceedings{yang2021graph,
title = {Graph Neural Networks Inspired by Classical Iterative Algorithms},
award = {Long Talk},
author = {Yang, Yongyi and Liu, Tang and Wang, Yangkun and Zhou, Jinjing and Gan, Quan and Wei, Zhewei and Zhang, Zheng and Huang*, Zengfeng and Wipf*, David},
booktitle = {International Conference on Machine Learning},
pages = {11773--11783},
year = {2021},
organization = {PMLR},
arxiv = {2103.06064},
html = {https://proceedings.mlr.press/v139/yang21g.html},
abbr = {ICML},
code = {https://github.com/FFTYYY/TWIRLS}
}
@inproceedings{hou2021massively,
title = {Massively Parallel Algorithms for Personalized PageRank},
volume = {14},
ISSN = {2150-8097},
DOI = {10.14778/3461535.3461554},
number = {9},
booktitle = {International Conference on Very Large Data Bases},
publisher = {Association for Computing Machinery (ACM)},
author = {Hou, Guanhao and Chen, Xingguang and Wang*, Sibo and Wei*, Zhewei},
year = {2021},
month = {may},
pages = {1668–1680},
html = {https://dl.acm.org/doi/10.14778/3461535.3461554},
abbr = {VLDB}
}
@inproceedings{wu2021unifying,
title = {Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push},
booktitle = {ACM Conference on Management of Data},
author = {Wu, Hao and Gan*, Junhao and Wei*, Zhewei and Zhang, Rui},
year = {2021},
abbr = {SIGMOD},
html = {https://dl.acm.org/doi/10.1145/3448016.3457298},
arxiv = {2101.03652},
code = {https://github.com/wuhao-wu-jiang/Personalized-PageRank}
}
@inproceedings{yan2021flashp,
title = {FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data},
html = {https://dl.acm.org/doi/10.14778/3446095.3446096},
author = {Yan, Shuyuan and Ding*, Bolin and Guo, Wei and Zhou, Jingren and Wei, Zhewei and Jiang, Xiaowei and Xu, Sheng},
booktitle = {International Conference on Very Large Data Bases},
year = {2021},