Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/81231
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Dai, Q | en_US |
dc.creator | Shen, X | en_US |
dc.creator | Zhang, L | en_US |
dc.creator | Li, Q | en_US |
dc.creator | Wang, D | en_US |
dc.date.accessioned | 2019-08-23T08:29:51Z | - |
dc.date.available | 2019-08-23T08:29:51Z | - |
dc.identifier.isbn | 9781450366748 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/81231 | - |
dc.description | 2019 World Wide Web Conference, WWW 2019, United States, 13-17 May 2019 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Association for Computing Machinery, Inc | en_US |
dc.rights | © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License(https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution. | en_US |
dc.rights | The following publication Dai, Q., Shen, X., Zhang, L., Li, Q., & Wang, D. (2019, May). Adversarial training methods for network embedding. In The World Wide Web Conference (pp. 329-339) is available at https://doi.org/10.1145/3308558.3313445 | en_US |
dc.subject | Adversarial Training | en_US |
dc.subject | Network Embedding | en_US |
dc.subject | Robustness | en_US |
dc.title | Adversarial training methods for network embedding | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 329 | en_US |
dc.identifier.epage | 339 | en_US |
dc.identifier.doi | 10.1145/3308558.3313445 | en_US |
dcterms.abstract | Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classification. Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process. Most recently, generative adversarial networks (GANs) based regularization methods are exploited to regularize embedding learning process, which can encourage a global smoothness of embedding vectors. These methods have very complicated architecture and suffer from the well-recognized non-convergence problem of GANs. In this paper, we aim to introduce a more succinct and effective local regularization method, namely adversarial training, to network embedding so as to achieve model robustness and better generalization performance. Firstly, the adversarial training method is applied by defining adversarial perturbations in the embedding space with an adaptive L2 norm constraint that depends on the connectivity pattern of node pairs. Though effective as a regularizer, it suffers from the interpretability issue which may hinder its application in certain real-world scenarios. To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain. These two regularization methods can be applied to many existing embedding models, and we take DeepWalk as the base model for illustration in the paper. Empirical evaluations in both link prediction and node classification demonstrate the effectiveness of the proposed methods. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019, p. 329-339 | en_US |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85066884164 | - |
dc.relation.conference | World Wide Web Conference | en_US |
dc.description.validate | 201908 bcma | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Dai_Adversarial_Training_Methods.pdf | 1.14 MB | Adobe PDF | View/Open |
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