research-article

Open access

Graph Alignment with Noisy Supervision

Authors:

Xiangliang ZhangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1104 - 1114

https://doi.org/10.1145/3485447.3512089

Published: 25 April 2022 Publication History

All formats PDF

Abstract

Recent years have witnessed increasing attention on the application of graph alignment to on-Web tasks, such as knowledge graph integration and social network linking. Despite achieving remarkable performance, prevailing graph alignment models still suffer from noisy supervision, yet how to mitigate the impact of noise in labeled data is still under-explored. The negative sampling based noise discrimination model has been a feasible solution to detect the noisy data and filter them out. However, due to its sensitivity to the sampling distribution, the negative sampling based noise discrimination model would lead to an inaccurate decision boundary. Furthermore, it is difficult to find an abiding threshold to separate the potential positive (benign) and negative (noisy) data in the whole training process. To address these important issues, in this paper, we design a non-sampling discrimination model resorting to the unbiased risk estimation of positive-unlabeled learning to circumvent the harmful impact of negative sampling. We also propose to select the appropriate potential positive data at different training stages by an adaptive filtration threshold enabled by curriculum learning, for maximally improving the performance of alignment model and non-sampling discrimination model. Extensive experiments conducted on several real-world datasets validate the effectiveness of our proposed method.

References

[1]

Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, and Xiangliang Zhang. 2020. Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation. In NeurIPS. 1–10.

[2]

Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, 2017. A closer look at memorization in deep networks. In ICML. 233–242.

[3]

Jessa Bekker and Jesse Davis. 2018. Estimating the class prior in positive and unlabeled data through decision tree induction. In AAAI. 2712–2719.

[4]

Jessa Bekker and Jesse Davis. 2020. Learning from positive and unlabeled data: a survey.Machine Learning 109, 4 (2020), 719–760.

[5]

Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009. Curriculum learning. In ICML. 41–48.

[6]

David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. In NeurIPS. 1–10.

[7]

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural network. In ICML. 1613–1622.

[8]

Yixin Cao, Zhiyuan Liu, Chengjiang Li, Juanzi Li, and Tat-Seng Chua. 2019. Multi-Channel Graph Neural Network for Entity Alignment. In ACL. 1452–1461.

[9]

Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, and Shaoping Ma. 2019. An efficient adaptive transfer neural network for social-aware recommendation. In SIGIR. 225–234.

[10]

Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, and Carlo Zaniolo. 2018. Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment. In IJCAI. 3998–4004.

[11]

Muhao Chen, Yingtao Tian, Mohan Yang, and Carlo Zaniolo. 2017. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In IJCAI. 1511–1517.

[12]

Marthinus Christoffel, Gang Niu, and Masashi Sugiyama. 2016. Class-prior Estimation for Learning from Positive and Unlabeled Data. In ACML. 221–236.

[13]

Xiaokai Chu, Xinxin Fan, Di Yao, Zhihua Zhu, Jianhui Huang, and Jingping Bi. 2019. Cross-network embedding for multi-network alignment. In Web Conf.273–284.

Digital Library

[14]

Marthinus C Du Plessis, Gang Niu, and Masashi Sugiyama. 2014. Analysis of learning from positive and unlabeled data. In NeurIPS. 703–711.

[15]

Charles Elkan and Keith Noto. 2008. Learning classifiers from only positive and unlabeled data. In KDD. 213–220.

[16]

Jie Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, and Depeng Jin. 2019. Dplink: User identity linkage via deep neural network from heterogeneous mobility data. In Web Conf.459–469.

Digital Library

[17]

Matthias Fey, Jan E Lenssen, Christopher Morris, Jonathan Masci, and Nils M Kriege. 2019. Deep Graph Matching Consensus. In ICLR.

[18]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In NeurIPS. 2672–2680.

[19]

Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu. 2017. Automated curriculum learning for neural networks. In ICML. 1311–1320.

[20]

Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang. 2018. Curriculumnet: Weakly supervised learning from large-scale web images. In ECCV. 135–150.

[21]

Pietro Hiram Guzzi and Tijana Milenković. 2018. Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin. Briefings in Bioinformatics 19, 3 (2018), 472–481.

[22]

Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NeurIPS. 8527–8537.

[23]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Web Conf.173–182.

Digital Library

[24]

Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra. 2018. Regal: Representation learning-based graph alignment. In CIKM. 117–126.

Digital Library

[25]

Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley. 2013. Stochastic variational inference.JMLR 14, 5 (2013).

[26]

Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. 2018. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In ICML. 2304–2313.

[27]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).

[28]

Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, and Masashi Sugiyama. 2017. Positive-unlabeled learning with non-negative risk estimator. In NeurIPS. 1675–1685.

[29]

Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, and Tat-Seng Chua. 2019. Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model. In EMNLP-IJCNLP. 2723–2732.

[30]

Chaozhuo Li, Senzhang Wang, Hao Wang, Yanbo Liang, Philip S Yu, Zhoujun Li, and Wei Wang. 2019. Partially shared adversarial learning for semi-supervised multi-platform user identity linkage. In CIKM. 249–258.

[31]

Chaozhuo Li, Senzhang Wang, Philip S Yu, Lei Zheng, Xiaoming Zhang, Zhoujun Li, and Yanbo Liang. 2018. Distribution distance minimization for unsupervised user identity linkage. In CIKM. 447–456.

[32]

Xiaoli Li and Bing Liu. 2003. Learning to classify texts using positive and unlabeled data. In IJCAI. 587–592.

[33]

Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, and Philip S Yu. 2003. Building text classifiers using positive and unlabeled examples. In ICDM. 179–186.

[34]

Bing Liu, Wee Sun Lee, Philip S Yu, and Xiaoli Li. 2002. Partially supervised classification of text documents. In ICML. 387–394.

[35]

Li Liu, William K Cheung, Xin Li, and Lejian Liao. 2016. Aligning Users across Social Networks Using Network Embedding. In IJCAI. 1774–1780.

[36]

Tongliang Liu and Dacheng Tao. 2015. Classification with noisy labels by importance reweighting. TPAMI 38, 3 (2015), 447–461.

Digital Library

[37]

Yangwei Liu, Hu Ding, Danyang Chen, and Jinhui Xu. 2017. Novel geometric approach for global alignment of PPI networks. In Proceedings of the AAAI Conference on Artificial Intelligence.

[38]

Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, and Tat-Seng Chua. 2020. Exploring and Evaluating Attributes, Values, and Structure for Entity Alignment. In EMNLP. 6355–6364.

[39]

Yueming Lyu and Ivor W Tsang. 2019. Curriculum Loss: Robust Learning and Generalization against Label Corruption. In ICLR.

[40]

Huda Nassar, Nate Veldt, Shahin Mohammadi, Ananth Grama, and David F Gleich. 2018. Low rank spectral network alignment. In Web Conf.619–628.

Digital Library

[41]

Shichao Pei, Lu Yu, Robert Hoehndorf, and Xiangliang Zhang. 2019. Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference. In Web Conf.3130–3136.

Digital Library

[42]

Shichao Pei, Lu Yu, Guoxian Yu, and Xiangliang Zhang. 2020. Rea: Robust cross-lingual entity alignment between knowledge graphs. In KDD. 2175–2184.

[43]

Shichao Pei, Lu Yu, and Xiangliang Zhang. 2019. Improving cross-lingual entity alignment via optimal transport. In IJCAI. 3231–3237.

[44]

Anastasia Pentina, Viktoriia Sharmanska, and Christoph H Lampert. 2015. Curriculum learning of multiple tasks. In CVPR. 5492–5500.

[45]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452–461.

Digital Library

[46]

Yanyao Shen and Sujay Sanghavi. 2019. Learning with bad training data via iterative trimmed loss minimization. In ICML. 5739–5748.

[47]

Xiaofei Shi and Yanghua Xiao. 2019. Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment. In EMNLP-IJCNLP. 813–822.

[48]

Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani, and Huan Liu. 2017. User identity linkage across online social networks: A review. ACM SIGKDD Explorations Newsletter 18, 2 (2017), 5–17.

Digital Library

[49]

Sen Su, Li Sun, Zhongbao Zhang, Gen Li, and Jielun Qu. 2018. MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In IJCAI. 3863–3869.

[50]

Sainbayar Sukhbaatar, Joan Bruna Estrach, Manohar Paluri, Lubomir Bourdev, and Robert Fergus. 2015. Training convolutional networks with noisy labels. In ICLR.

[51]

Zequn Sun, Wei Hu, and Chengkai Li. 2017. Cross-lingual entity alignment via joint attribute-preserving embedding. In ISWC. 628–644.

[52]

Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu. 2018. Bootstrapping Entity Alignment with Knowledge Graph Embedding. In IJCAI. 4396–4402.

[53]

Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, and Yuzhong Qu. 2020. Knowledge graph alignment network with gated multi-hop neighborhood aggregation. In AAAI. 222–229.

[54]

Brendan Van Rooyen and Robert C Williamson. 2017. A Theory of Learning with Corrupted Labels.JMLR 18, 1 (2017), 8501–8550.

[55]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.

[56]

Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. Irgan: A minimax game for unifying generative and discriminative information retrieval models. In SIGIR. 515–524.

Digital Library

[57]

Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. 2018. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. In EMNLP. 349–357.

[58]

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, and Dongyan Zhao. 2019. Relation-aware entity alignment for heterogeneous knowledge graphs. In IJCAI. 5278–5284.

[59]

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, and Dongyan Zhao. 2020. Neighborhood Matching Network for Entity Alignment. In ACL. 6477––6487.

[60]

Wei Xie, Xin Mu, Roy Ka-Wei Lee, Feida Zhu, and Ee-Peng Lim. 2018. Unsupervised user identity linkage via factoid embedding. In ICDM. 1338–1343.

[61]

Xin Xin, Fajie Yuan, Xiangnan He, and Joemon M Jose. 2018. Batch is not heavy: Learning word representations from all samples. In ACL. 1853–1862.

[62]

Hongteng Xu, Dixin Luo, Hongyuan Zha, and Lawrence Carin Duke. 2019. Gromov-wasserstein learning for graph matching and node embedding. In ICML. 6932–6941.

[63]

Miao Xu, Bingcong Li, Gang Niu, Bo Han, and Masashi Sugiyama. 2019. Revisiting sample selection approach to positive-unlabeled learning: Turning unlabeled data into positive rather than negative. arXiv preprint arXiv:1901.10155(2019).

[64]

Abdurrahman Yasar and Ümit V Çatalyürek. 2018. An iterative global structure-assisted labeled network aligner. In KDD. 2614–2623.

[65]

Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. In ICLR.

[66]

Qingheng Zhang, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, and Yuzhong Qu. 2019. Multi-view knowledge graph embedding for entity alignment. In IJCAI. 5429–5435.

[67]

Si Zhang and Hanghang Tong. 2016. Final: Fast attributed network alignment. In KDD. 1345–1354.

[68]

Si Zhang, Hanghang Tong, Ross Maciejewski, and Tina Eliassi-Rad. 2019. Multilevel network alignment. In Web Conf.2344–2354.

Digital Library

[69]

Yutao Zhang, Jie Tang, Zhilin Yang, Jian Pei, and Philip S Yu. 2015. Cosnet: Connecting heterogeneous social networks with local and global consistency. In KDD. 1485–1494.

Digital Library

[70]

Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, and Dejing Dou. 2020. Adversarial attacks on deep graph matching. NeurIPS 33(2020).

[71]

Zexuan Zhong, Yong Cao, Mu Guo, and Zaiqing Nie. 2018. Colink: An unsupervised framework for user identity linkage. In AAAI.

[72]

Hao Zhu, Ruobing Xie, Zhiyuan Liu, and Maosong Sun. 2017. Iterative entity alignment via joint knowledge embeddings. In IJCAI. 4258–4264.

[73]

Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, and Li Guo. 2019. Neighborhood-aware attentional representation for multilingual knowledge graphs. In IJCAI. 10–16.

Cited By

Long MChen SWang J(2024)Locally-adaptive mapping for network alignment via meta-learningInformation Processing & Management10.1016/j.ipm.2024.10381761:5(103817)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103817
Sun LDu YGao SYe JWang FRen FLiang MWang YWang S(2023)GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group AlignmentACM Transactions on the Web10.1145/358050917:3(1-30)Online publication date: 22-May-2023
https://dl.acm.org/doi/10.1145/3580509
Tang WSun HWang JQi QWang JYang HTao SChen HDuh WHuang HKato MMothe JPoblete B(2023)Multi-order Matched Neighborhood Consistent Graph Alignment in a Union Vector SpaceProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591735(963-972)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591735
Show More Cited By

Index Terms

Graph Alignment with Noisy Supervision

Index terms have been assigned to the content through auto-classification.

Recommendations

Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU learning is to sample a set of pseudo-negatives from the unlabeled data ...
A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions
Proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 9422

Standard supervised classification learns a classifier from a set of labeled examples. Alternatively, in the field of weakly supervised classification different frameworks have been presented where the training data cannot be certainly labeled. In this ...
Fine-grained label learning in object detection with weak supervision of captions
Abstract
This paper addresses the task of fine-grained label learning in object detection with the weak supervision of auxiliary information attached to images. Most of the recent work focused on the label prediction for objects in the same category space ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2022

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '22

Sponsor:

SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
1,404
Total Downloads

Downloads (Last 12 months)372
Downloads (Last 6 weeks)63

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Long MChen SWang J(2024)Locally-adaptive mapping for network alignment via meta-learningInformation Processing & Management10.1016/j.ipm.2024.10381761:5(103817)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103817
Sun LDu YGao SYe JWang FRen FLiang MWang YWang S(2023)GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group AlignmentACM Transactions on the Web10.1145/358050917:3(1-30)Online publication date: 22-May-2023
https://dl.acm.org/doi/10.1145/3580509
Tang WSun HWang JQi QWang JYang HTao SChen HDuh WHuang HKato MMothe JPoblete B(2023)Multi-order Matched Neighborhood Consistent Graph Alignment in a Union Vector SpaceProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591735(963-972)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591735
Yang XWang HSun JZhang SChen CHua XLuo X(2023)Prototypical Mixing and Retrieval-based Refinement for Label Noise-resistant Image Retrieval2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01032(11205-11215)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.01032
Qian YPan L(2023)Variety-aware GAN and online learning augmented self-training model for knowledge graph entity alignmentInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10347260:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103472
Xie FZeng XZhou BTan Y(2023)Improving Knowledge Graph Entity Alignment with Graph AugmentationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33377-4_1(3-14)Online publication date: 25-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-33377-4_1

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents