2023

Towards Data-centric Graph Machine Learning: Review and Outlook

Zheng, Xin, Liu, Yixin, Bao, Zhifeng et al.

Understand

Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.

In this article, we conduct an in-depth and comprehensive review, offering a forward-looking outlook on the current efforts in data-centric AI pertaining to graph data-the fundamental data structure for representing and capturing intricate dependencies among massive and diverse real-life entities.
We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of the graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance.
A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-centric view.

Built on

Dynamic Self-training Framework for Graph Convolutional Networks
Ziang Zhou, Shengzhong Zhang, and Zengfeng Huang. 2019 · 1910
Earlier work this paper cites.
Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition . 1944–1952
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017 · 1952
Earlier work this paper cites.
The Fréchet distance between multivariate normal distributions
DC Dowson and BV666017 Landau. 1982 · 1982
Earlier work this paper cites.
Learning discrete structures for graph neural networks. In International Conference on Machine Learning . PMLR, 1972–1982
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019 · 1982
Earlier work this paper cites.
Access control: principle and practice
Ravi S Sandhu and Pierangela Samarati. 1994 · 1994
Earlier work this paper cites.
Estimation and prediction for stochastic blockmodels for graphs with latent block structure
Tom AB Snijders and Krzysztof Nowicki. 1997 · 1997
Earlier work this paper cites.
neo4j: Graph Database & Analytics
Inc. neo4j. 2000 · 2000
Earlier work this paper cites.
SMOTE: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002 · 2002
Earlier work this paper cites.
igraph: The Network Analysis Package
igraph core team. 2003 · 2003
Earlier work this paper cites.
Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Schölkopf. 2003 · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions. In International Conference on Machine Learning . 912–919
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. 2003 · 2003
Earlier work this paper cites.
Amazon Mechanical Turk
Amazon. 2005 · 2005
Earlier work this paper cites.
Learning from labeled and unlabeled data on a directed graph. In International Conference on Machine Learning . 1036–1043
Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf. 2005 · 2005
Earlier work this paper cites.
Label propagation through linear neighborhoods. In International Conference on Machine Learning . 985–992
Fei Wang and Changshui Zhang. 2006 · 2006
Earlier work this paper cites.
Towards identity anonymization on graphs. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data . 93–106
Kun Liu and Evimaria Terzi. 2008 · 2008
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Learning from imbalanced data
Haibo He and Edwardo A Garcia. 2009 · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman. 2009 · 2009
Earlier work this paper cites.
Herding dynamical weights to learn. In Proceedings of the 26th Annual International Conference on Machine Learning . 1121–1128
Max Welling. 2009 · 2009
Earlier work this paper cites.
icarl: Incremental classifier and representation learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2001–2010
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. 2017 · 2010
Earlier work this paper cites.
Privacy and security for online social networks: challenges and opportunities
Chi Zhang, Jinyuan Sun, Xiaoyan Zhu, and Yuguang Fang. 2010 · 2010
Earlier work this paper cites.
Local graph sparsification for scalable clustering. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data . 721–732
Venu Satuluri, Srinivasan Parthasarathy, and Yiye Ruan. 2011 · 2011
Earlier work this paper cites.
Spectral sparsification of graphs
Daniel A Spielman and Shang-Hua Teng. 2011 · 2011
Earlier work this paper cites.
Semi-supervised consensus labeling for crowdsourcing. In SIGIR 2011 Workshop on Crowdsourcing for Information Retrieval . 1–6
Wei Tang and Matthew Lease. 2011 · 2011
Earlier work this paper cites.
Spectral sparsification of graphs: theory and algorithms
Joshua Batson, Daniel A Spielman, Nikhil Srivastava, and Shang-Hua Teng. 2013 · 2013
Earlier work this paper cites.
Graph Classification with Imbalanced Class Distributions and Noise.. In Proceedings of International Joint Conference on Artificial Intelligence . 1586–1592
Shirui Pan and Xingquan Zhu. 2013 · 2013
Earlier work this paper cites.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013 · 2013
Earlier work this paper cites.
Implementation of elliptic curve digital signature algorithm (ECDSA). In 2014 Global Summit on Computer & Information Technology . IEEE, 1–6
Abdessalem Abidi, Belgacem Bouallegue, and Fatma Kahri. 2014 · 2014
Earlier work this paper cites.
Signal denoising on graphs via graph filtering. In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 872–876
Siheng Chen, Aliaksei Sandryhaila, José MF Moura, and Jelena Kovacevic. 2014 · 2014
Earlier work this paper cites.
NetworkX: Network Analysis in Python
NetworkX developers. 2014 · 2014
Earlier work this paper cites.
Focused clustering and outlier detection in large attributed graphs. In Proceedings of the 20th ACM SIGKDD international Conference on Knowledge Discovery & Data Mining . 1346–1355
Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, and Emmanuel Müller. 2014 · 2014
Earlier work this paper cites.
Privacy-Integrated Graph Clustering Through Differential Privacy. In EDBT/ICDT Workshops , Vol. 1330. 247–254
Yvonne Mülle, Chris Clifton, and Klemens Böhm. 2015 · 2015
Earlier work this paper cites.
Trend filtering on graphs. In Artificial Intelligence and Statistics . PMLR, 1042–1050
Yu-Xiang Wang, James Sharpnack, Alex Smola, and Ryan Tibshirani. 2015 · 2015
Earlier work this paper cites.
Edge classification in networks. In 2016 IEEE 32nd International Conference on Data Engineering . IEEE, 1038–1049
Charu Aggarwal, Gewen He, and Peixiang Zhao. 2016 · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Original
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Loss factorization, weakly supervised learning and label noise robustness. In International Conference on Machine Learning . PMLR, 708–717
Giorgio Patrini, Frank Nielsen, Richard Nock, and Marcello Carioni. 2016 · 2016
Earlier work this paper cites.
Graph sparsification approaches for laplacian smoothing. In Artificial Intelligence and Statistics . PMLR, 1250–1259
Veeru Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani. 2016 · 2016
Earlier work this paper cites.
Amazon Neptune AI Platform
Piotr Niedź wiedź. 2016 · 2016
Earlier work this paper cites.
Active learning for graph embedding
Original
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
Thomas N Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Focal loss for dense object detection. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition . 2980–2988
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017 · 2017
Earlier work this paper cites.
Deep transfer learning with joint adaptation networks. In International Conference on Machine Learning . PMLR, 2208–2217
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan. 2017 · 2017
Earlier work this paper cites.
Mgae: Marginalized graph autoencoder for graph clustering. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 889–898
Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. 2017 · 2017
Earlier work this paper cites.
Central moment discrepancy (cmd) for domain-invariant representation learning
Original
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz. 2017 · 2017
Earlier work this paper cites.
Domain Adaptation with Adversarial Training and Graph Embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics . 1077–1087
Firoj Alam, Shafiq Joty, and Muhammad Imran. 2018 · 2018
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features. In Proceedings of the European Conference on Computer Vision . 132–149
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018 · 2018
Earlier work this paper cites.
Active discriminative network representation learning. In International Joint Conference on Artificial Intelligence
Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu. 2018 · 2018
Earlier work this paper cites.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018 · 2018
Earlier work this paper cites.
Influence maximization on social graphs: A survey
Yuchen Li, Ju Fan, Yanhao Wang, and Kian-Lee Tan. 2018a · 2018
Earlier work this paper cites.
Deepeye: Towards automatic data visualization. In IEEE International Conference on Data Engineering . IEEE, 101–112
Yuyu Luo, Xuedi Qin, Nan Tang, and Guoliang Li. 2018 · 2018
Earlier work this paper cites.
Adversarially regularized graph autoencoder for graph embedding. In Proceedings of the 27th International Joint Conference on Artificial Intelligence . 2609–2615
Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. 2018 · 2018
Earlier work this paper cites.
Model evaluation, model selection, and algorithm selection in machine learning
Original
Sebastian Raschka. 2018 · 2018
Earlier work this paper cites.
Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations
Ozan Sener and Silvio Savarese. 2018 · 2018
Earlier work this paper cites.
Pitfalls of graph neural network evaluation
Original
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In International Conference on Learning Representations
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Graph polynomial filter for signal denoising
Waseem Waheed and David BH Tay. 2018 · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Original
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Privacy preservation based on clustering perturbation algorithm for social network
Fahong Yu, Meijia Chen, Bolin Yu, Wenping Li, Longhua Ma, and Huimin Gao. 2018 · 2018
Earlier work this paper cites.
Accelerating the machine learning lifecycle with MLflow
Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, et al · 2018
Earlier work this paper cites.
mixup: Beyond Empirical Risk Minimization. In International Conference on Learning Representations
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2018 · 2018
Earlier work this paper cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Earlier work this paper cites.
On differentially private graph sparsification and applications
Raman Arora and Jalaj Upadhyay. 2019 · 2019
Earlier work this paper cites.
Simgnn: A neural network approach to fast graph similarity computation. In Proceedings of the twelfth ACM International Conference on Web Search and Data Mining . 384–392
Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, and Wei Wang. 2019 · 2019
Earlier work this paper cites.
ActiveHNE: active heterogeneous network embedding. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 2123–2129
Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, and Xiangliang Zhang. 2019 · 2019
Earlier work this paper cites.

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Cited alongside, same era.
Diffusion improves graph learning
Johannes Gasteiger, Stefan Weißenberger, and Stephan Günnemann. 2019b · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning. In International Conference on Machine Learning . PMLR, 2242–2251
Amirata Ghorbani and James Zou. 2019 · 2019
Cited alongside, same era.
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 922–929
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019 · 2019
Cited alongside, same era.
Towards efficient data valuation based on the shapley value. In The 22nd International Conference on Artificial Intelligence and Statistics . PMLR, 1167–1176
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos. 2019 · 2019
Cited alongside, same era.
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Cited alongside, same era.
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Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel. 2019 · 2019
Cited alongside, same era.
Learning graph neural networks with noisy labels
Original
Hoang NT, Choong Jun Jin, and Tsuyoshi Murata. 2019 · 2019
Cited alongside, same era.
Data validation for machine learning
Neoklis Polyzotis, Martin Zinkevich, Sudip Roy, Eric Breck, and Steven Whang. 2019 · 2019
Cited alongside, same era.
Continuous integration of machine learning models with ease. ml/ci: Towards a rigorous yet practical treatment
Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, and Ce Zhang. 2019 · 2019
Cited alongside, same era.
Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Cited alongside, same era.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
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Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019 · 2019
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Real-time Fraud Detection with Graph Neural Network on DGL
Amazon Web Services. 2021 · 2021
Later among the works it cites.
Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
Joakim Skarding, Bogdan Gabrys, and Katarzyna Musial. 2021 · 2021
Later among the works it cites.
Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021 · 2021
Later among the works it cites.
Graph convolutional networks for graphs containing missing features
Hibiki Taguchi, Xin Liu, and Tsuyoshi Murata. 2021 · 2021
Later among the works it cites.
Siamese Attribute-missing Graph Auto-encoder
Original
Wenxuan Tu, Sihang Zhou, Yue Liu, and Xinwang Liu. 2021 · 2021
Later among the works it cites.
Data-Centric AI Virtual Workshop
Stanford University. 2021 · 2021
Later among the works it cites.
Graphmix: Improved training of gnns for semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 10024–10032
Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang. 2021 · 2021
Later among the works it cites.
Contrastive graph poisson networks: Semi-supervised learning with extremely limited labels
Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, and Chen Gong. 2021b · 2021
Later among the works it cites.
Combining graph convolutional neural networks and label propagation
Hongwei Wang and Jure Leskovec. 2021 · 2021
Later among the works it cites.
Graph contrastive learning automated. In International Conference on Machine Learning . PMLR, 12121–12132
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021 · 2021
Later among the works it cites.
Alg: Fast and accurate active learning framework for graph convolutional networks. In Proceedings of the 2021 International Conference on Management of Data . 2366–2374
Wentao Zhang, Yu Shen, Yang Li, Lei Chen, Zhi Yang, and Bin Cui. 2021a · 2021
Later among the works it cites.
Rim: Reliable influence-based active learning on graphs
Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, and Bin Cui. 2021b · 2021
Later among the works it cites.
GRAIN: improving data efficiency of graph neural networks via diversified in fluence maximization
Wentao Zhang, Zhi Yang, Yexin Wang, Yu Shen, Yang Li, Liang Wang, and Bin Cui. 2021c · 2021
Later among the works it cites.
Adaptive diffusion in graph neural networks
Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, and Jie Tang. 2021a · 2021
Later among the works it cites.
Attent: Active attributed network alignment. In Proceedings of the Web Conference . 3896–3906
Qinghai Zhou, Liangyue Li, Xintao Wu, Nan Cao, Lei Ying, and Hanghang Tong. 2021 · 2021
Later among the works it cites.
Shift-robust gnns: Overcoming the limitations of localized graph training data
Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi. 2021a · 2021
Later among the works it cites.
Regularizing graph neural networks via consistency-diversity graph augmentations. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 3913–3921
Deyu Bo, BinBin Hu, Xiao Wang, Zhiqiang Zhang, Chuan Shi, and Jun Zhou. 2022 · 2022
Later among the works it cites.
The Effects of Data Quality on Machine Learning Performance
Original
Lukas Budach, Moritz Feuerpfeil, Nina Ihde, Andrea Nathansen, Nele Noack, Hendrik Patzlaff, Hazar Harmouch, and Felix Naumann. 2022 · 2022
Later among the works it cites.
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. In Advances in Neural Information Processing Systems
Davide Buffelli, Pietro Lio, and Fabio Vandin. 2022 · 2022
Later among the works it cites.
ALLIE: Active Learning on Large-scale Imbalanced Graphs. In Proceedings of the ACM Web Conference . 690–698
Limeng Cui, Xianfeng Tang, Sumeet Katariya, Nikhil Rao, Pallav Agrawal, Karthik Subbian, and Dongwon Lee. 2022 · 2022
Later among the works it cites.
Towards robust graph neural networks for noisy graphs with sparse labels. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . 181–191
Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang. 2022 · 2022
Later among the works it cites.
Data augmentation for deep graph learning: A survey
Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu. 2022c · 2022
Later among the works it cites.
Faster Hyperparameter Search on Graphs via Calibrated Dataset Condensation. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning
Mucong Ding, Xiaoyu Liu, Tahseen Rabbani, and Furong Huang. 2022a · 2022
Later among the works it cites.
Location-centered house price prediction: A multi-task learning approach
Guangliang Gao, Zhifeng Bao, Jie Cao, A Kai Qin, and Timos Sellis. 2022 · 2022
Later among the works it cites.
G-mixup: Graph data augmentation for graph classification. In International Conference on Machine Learning . PMLR, 8230–8248
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu. 2022 · 2022
Later among the works it cites.
GraphMAE: Self-Supervised Masked Graph Autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 594–604
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022 · 2022
Later among the works it cites.
Amer: A New Attribute-Missing Network Embedding Approach
Di Jin, Rui Wang, Tao Wang, Dongxiao He, Weiping Ding, Yuxiao Huang, Longbiao Wang, and Witold Pedrycz. 2022c · 2022
Later among the works it cites.
JuryGCN: quantifying jackknife uncertainty on graph convolutional networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 742–752
Jian Kang, Qinghai Zhou, and Hanghang Tong. 2022 · 2022
Later among the works it cites.
Robust optimization as data augmentation for large-scale graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 60–69
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein. 2022 · 2022
Later among the works it cites.
Cyclic label propagation for graph semi-supervised learning
Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, and Jiajun Bu. 2022 · 2022
Later among the works it cites.
Graph self-supervised learning: A survey
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip Yu. 2022a · 2022
Later among the works it cites.
Graphworld: Fake graphs bring real insights for gnns. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 3691–3701
John Palowitch, Anton Tsitsulin, Brandon Mayer, and Bryan Perozzi. 2022 · 2022
Later among the works it cites.
A new self-supervised task on graphs: Geodesic distance prediction
Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, and Qinghua Zheng. 2022 · 2022
Later among the works it cites.
Untrained graph neural networks for denoising
Samuel Rey, Santiago Segarra, Reinhard Heckel, and Antonio G Marques. 2022 · 2022
Later among the works it cites.
Synthetic Graph Generation to Benchmark Graph Learning
Original
Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, and Bryan Perozzi. 2022 · 2022
Later among the works it cites.
A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and S Yu Philip. 2022a · 2022
Later among the works it cites.
A Generic Graph Sparsification Framework using Deep Reinforcement Learning. In 2022 IEEE International Conference on Data Mining . IEEE, 1221–1226
Ryan Wickman, Xiaofei Zhang, and Weizi Li. 2022 · 2022
Later among the works it cites.
Discovering invariant rationales for graph neural networks
Original
Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. 2022 · 2022
Later among the works it cites.
Trustworthy graph neural networks: Aspects, methods and trends
Original
He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, and Jian Pei. 2022c · 2022
Later among the works it cites.
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Original
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, and Suhang Wang. 2022 · 2022
Later among the works it cites.
Graph neural networks for graphs with heterophily: A survey
Original
Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu. 2022 · 2022
Later among the works it cites.
Shift-Robust Node Classification via Graph Clustering Co-training. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning
Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, and Jiawei Han. 2022 · 2022
Later among the works it cites.
Demystifying Artificial Intelligence for Data Preparation. In Companion of International Conference on Management of Data . 13–20
Chengliang Chai, Nan Tang, Ju Fan, and Yuyu Luo. 2023 · 2023
Closest in time.
A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability
Yuxin Guo, Cheng Yang, Yuluo Chen, Jixi Liu, Chuan Shi, and Junping Du. 2023 · 2023
Closest in time.
Deep entity matching with adversarial active learning
Jiacheng Huang, Wei Hu, Zhifeng Bao, Qijin Chen, and Yuzhong Qu. 2023 · 2023
Closest in time.
T2-gnn: Graph neural networks for graphs with incomplete features and structure via teacher-student distillation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 4339–4346
Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang, and Lingfei Wu. 2023 · 2023
Closest in time.
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Original
Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I Webb, Irwin King, and Shirui Pan. 2023 · 2023
Closest in time.
Machine learning operations (mlops): Overview, definition, and architecture
Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl. 2023 · 2023
Closest in time.
Informative pseudo-labeling for graph neural networks with few labels
Yayong Li, Jie Yin, and Ling Chen. 2023 · 2023
Closest in time.
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Original
Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang. 2023b · 2023
Closest in time.
Graph clustering with graph neural networks
Emmanuel Müller. 2023 · 2023
Closest in time.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Original
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023 · 2023
Closest in time.
Robust Training of Graph Neural Networks via Noise Governance. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining . 607–615
Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z Chen, and Jian Wu. 2023 · 2023
Closest in time.
Graphstorm: enterprise graph machine learning framework for billion-scale graphs for ML scientists and data scientists
Amazon Web Services. 2023 · 2023
Closest in time.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. In Proceedings of the AAAI Conference on Artificial Intelligence
Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. 2023 · 2023
Closest in time.
CLNode: Curriculum Learning for Node Classification. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining . 670–678
Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, and Wenbin Hu. 2023 · 2023
Closest in time.
Data collection and quality challenges in deep learning: A data-centric ai perspective
Steven Euijong Whang, Yuji Roh, Hwanjun Song, and Jae-Gil Lee. 2023 · 2023
Closest in time.
Data-centric AI: Perspectives and Challenges
Original
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, and Xia Hu. 2023a · 2023
Closest in time.
Data-centric artificial intelligence: A survey
Original
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu. 2023b · 2023
Closest in time.
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Original
Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan. 2023 · 2023
Closest in time.
Robust graph representation learning for local corruption recovery. In Proceedings of the Web Conference
Bingxin Zhou, Yuanhong Jiang, Yu Guang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, and Xiaoqun Zhang. 2023 · 2023
Closest in time.
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification
Original
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Imbalanced graph classification via graph-of-graph neural networks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 2067–2076
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Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference . 2069–2080
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