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Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.

- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.3 pp.568-578

- Publication Date
- 2019/03/01

- Publicized
- 2018/12/26

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.2018EDP7233

- Type of Manuscript
- PAPER

- Category
- Artificial Intelligence, Data Mining

Xiaotao CHENG

National Digital Switching System Engineering & Technological R&D Center

Lixin JI

National Digital Switching System Engineering & Technological R&D Center

Ruiyang HUANG

National Digital Switching System Engineering & Technological R&D Center

Ruifei CUI

Radboud University Nijmegen

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Xiaotao CHENG, Lixin JI, Ruiyang HUANG, Ruifei CUI, "Network Embedding with Deep Metric Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 3, pp. 568-578, March 2019, doi: 10.1587/transinf.2018EDP7233.

Abstract: Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7233/_p

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@ARTICLE{e102-d_3_568,

author={Xiaotao CHENG, Lixin JI, Ruiyang HUANG, Ruifei CUI, },

journal={IEICE TRANSACTIONS on Information},

title={Network Embedding with Deep Metric Learning},

year={2019},

volume={E102-D},

number={3},

pages={568-578},

abstract={Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.},

keywords={},

doi={10.1587/transinf.2018EDP7233},

ISSN={1745-1361},

month={March},}

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TY - JOUR

TI - Network Embedding with Deep Metric Learning

T2 - IEICE TRANSACTIONS on Information

SP - 568

EP - 578

AU - Xiaotao CHENG

AU - Lixin JI

AU - Ruiyang HUANG

AU - Ruifei CUI

PY - 2019

DO - 10.1587/transinf.2018EDP7233

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E102-D

IS - 3

JA - IEICE TRANSACTIONS on Information

Y1 - March 2019

AB - Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.

ER -