The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
네트워크 임베딩은 정점 분류, 커뮤니티 탐지, 네트워크 시각화 등 그래프 마이닝 작업에 광범위하게 적용되기 때문에 최근 몇 년간 주목을 받고 있습니다. 네트워크 임베딩은 네트워크 구조를 캡처하고 보존하는 것을 목표로 네트워크에서 정점의 저차원 표현을 학습하는 중요한 방법입니다. 거의 모든 기존 네트워크 임베딩 방법은 Word2vec에서 소위 Skip-gram 모델을 채택합니다. 그러나 스킵그램 모델은 bag-of-words 모델로서 주로 지역적 구조 정보를 활용하였다. 글로벌 네트워크의 정점에 대한 정보 메트릭이 부족하면 새로운 임베딩 공간에서 서로 다른 레이블을 가진 정점이 혼합됩니다. 이러한 문제를 해결하기 위해 본 논문에서는 Deep Metric Learning을 이용한 네트워크 표현 학습 방법인 DML-NRL을 제안합니다. 초기화된 앵커 정점을 설정하고 훈련 진행에 유사성 측정을 추가함으로써 네트워크에 있는 서로 다른 정점 레이블 간의 거리 정보가 정점 표현에 통합되어 네트워크 임베딩 알고리즘의 정확도가 효과적으로 향상됩니다. 우리는 우리의 방법을 다중 레이블 분류 및 정점의 데이터 시각화 작업에 적용하여 기준선과 비교합니다. 실험 결과는 우리의 방법이 세 가지 데이터 세트 모두에서 기준선보다 성능이 뛰어나다는 것을 보여 주며 이 방법은 효과적이고 견고한 것으로 입증되었습니다.
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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부
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
부
@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},}
부
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 -