research-article

Active Domain Transfer on Network Embedding

Authors:

Yilun JinAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2683 - 2689

https://doi.org/10.1145/3366423.3380024

Published: 20 April 2020 Publication History

Abstract

Recent works show that end-to-end, (semi-) supervised network embedding models can generate satisfactory vectors to represent network topology, and are even applicable to unseen graphs by inductive learning. However, domain mismatch between training and testing network for inductive learning, as well as lack of labeled data often compromises the outcome of such methods. To make matters worse, while transfer learning and active learning techniques, being able to solve such problems correspondingly, have been well studied on regular i.i.d data, relatively few attention has been paid on networks. Consequently, we propose in this paper a method for active transfer learning on networks named active-transfer network embedding, abbreviated ATNE. In ATNE we jointly consider the influence of each node on the network from the perspectives of transfer and active learning, and hence design novel and effective influence scores combining both aspects in the training process to facilitate node selection. We demonstrate that ATNE is efficient and decoupled from the actual model used. Further extensive experiments show that ATNE outperforms state-of-the-art active node selection methods and shows versatility in different situations.

References

[1]

Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2007. Analysis of representations for domain adaptation. In Advances in neural information processing systems. 137–144.

[2]

John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman. 2008. Learning bounds for domain adaptation. In Advances in neural information processing systems. 129–136.

[3]

Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2017. Active learning for graph embedding. arXiv preprint arXiv:1705.05085(2017).

[4]

Yee Seng Chan and Hwee Tou Ng. 2007. Domain adaptation with active learning for word sense disambiguation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 49–56.

[5]

Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247(2018).

[6]

Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, and Xiangliang Zhang. 2019. ActiveHNE: Active Heterogeneous Network Embedding. arXiv preprint arXiv:1905.05659(2019).

[7]

Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018. A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering 31, 5(2018), 833–852.

[8]

Lun Du, Zhicong Lu, Yun Wang, Guojie Song, Yiming Wang, and Wei Chen. 2018. Galaxy Network Embedding: A Hierarchical Community Structure Preserving Approach. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, 2079–2085. https://doi.org/10.24963/ijcai.2018/287

[9]

Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang. 2018. Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, 2086–2092. https://doi.org/10.24963/ijcai.2018/288

[10]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024–1034.

[11]

Jing Jiang. 2008. A literature survey on domain adaptation of statistical classifiers. URL: http://sifaka. cs. uiuc. edu/jiang4/domainadaptation/survey 3(2008), 1–12.

[12]

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

[13]

David D Lewis and William A Gale. 1994. A sequential algorithm for training text classifiers. In SIGIR’94. Springer, 3–12.

[14]

Ziyao Li, Liang Zhang, and Guojie Song. 2018. SepNE: Bringing Separability to Network Embedding. CoRR abs/1811.05614(2018). arxiv:1811.05614http://arxiv.org/abs/1811.05614

[15]

Ziyao Li, Liang Zhang, and Guojie Song. 2019. GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 2959–2965. https://doi.org/10.24963/ijcai.2019/410

[16]

Qingqing Long, Yiming Wang, Lun Du, Guojie Song, Yilun Jin, and Wei Lin. 2019. Hierarchical Community Structure Preserving Network Embedding: A Subspace Approach. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management(CIKM ’19). Association for Computing Machinery, New York, NY, USA, 409–418. https://doi.org/10.1145/3357384.3357947

Digital Library

[17]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579–2605.

[18]

Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10(2009), 1345–1359.

Digital Library

[19]

Piyush Rai, Avishek Saha, Hal Daumé III, and Suresh Venkatasubramanian. 2010. Domain adaptation meets active learning. In Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing. Association for Computational Linguistics, 27–32.

Digital Library

[20]

Burr Settles. 2009. Active learning literature survey. Technical Report. University of Wisconsin-Madison Department of Computer Sciences.

[21]

Jie Tang. 2016. AMiner: Mining deep knowledge from big scholar data. In Proceedings of the 25th international conference companion on world wide web. International World Wide Web Conferences Steering Committee, 373–373.

Digital Library

[22]

Nenad Tomasev, Milos Radovanovic, Dunja Mladenic, and Mirjana Ivanovic. 2013. The role of hubness in clustering high-dimensional data. IEEE Transactions on Knowledge and Data Engineering 26, 3(2013), 739–751.

Digital Library

[23]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903(2017).

[24]

Yun Wang, Lun Du, Guojie Song, Xiaojun Ma, Lichen Jin, Wei Lin, and Fei Sun. 2019. Tag2Gauss: Learning Tag Representations via Gaussian Distribution in Tagged Networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 3799–3805. https://doi.org/10.24963/ijcai.2019/527

[25]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596(2019).

[26]

Yizhou Zhang, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. 2019. DANE: Domain Adaptive Network Embedding. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 4362–4368. https://doi.org/10.24963/ijcai.2019/606

Cited By

Mao HDu LZheng YFu QLi ZChen XHan SZhang DAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Source Free Graph Unsupervised Domain AdaptationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635802(520-528)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635802
Zhang YZheng ZNevatia RLiu Y(2022)Improving Weakly Supervised Scene Graph Parsing through Object Grounding2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956641(4058-4064)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956641

Index Terms

Active Domain Transfer on Network Embedding
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications

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

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Cited By

Mao HDu LZheng YFu QLi ZChen XHan SZhang DAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Source Free Graph Unsupervised Domain AdaptationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635802(520-528)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635802
Zhang YZheng ZNevatia RLiu Y(2022)Improving Weakly Supervised Scene Graph Parsing through Object Grounding2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956641(4058-4064)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956641

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