Modularity-based representation learning for networks

doi:10.1088/1674-1056/abbbec

中国物理B ›› 2020, Vol. 29 ›› Issue (12): 128901-.doi: 10.1088/1674-1056/abbbec

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇下一篇

收稿日期:2020-07-17 修回日期:2020-08-31 接受日期:2020-09-28 出版日期:2020-12-01 发布日期:2020-12-02

Modularity-based representation learning for networks

Jialin He(何嘉林)^1,3,†, Dongmei Li(李冬梅)², and Yuexi Liu(刘阅希)¹

¹ School of Computer Science and Engineering, China West Normal University, Nanchong 637009, China; ² Department of Scientific Research, China West Normal University, Nanchong 637009, China; ³ Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong, Nanchong \/637009, China

Received:2020-07-17 Revised:2020-08-31 Accepted:2020-09-28 Online:2020-12-01 Published:2020-12-02
Contact: ^†Corresponding author. E-mail: hejialin32@126.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 61673085), the Program from the Sichuan Provincial Science and Technology, China (Grant No. 2018RZ0081), and the Fundamental Research Funds of China West Normal University (Grant No. 17E063).

摘要/Abstract

Abstract: Network embedding aims at learning low-dimensional representation of vertexes in a network and effectively preserving network structures. These representations can be used as features for many complex tasks on networks such as community detection and multi-label classification. Some classic methods based on the skip-gram model have been proposed to learn the representation of vertexes. However, these methods do not consider the global structure ( i.e., community structure) while sampling vertex sequences in network. To solve this problem, we suggest a novel sampling method which takes community information into consideration. It first samples dense vertex sequences by taking advantage of modularity function and then learns vertex representation by using the skip-gram model. Experimental results on the tasks of community detection and multi-label classification show that our method outperforms three state-of-the-art methods on learning the vertex representations in networks.

Key words: network embedding, low-dimensional representation, vertex sequences, community detection

中图分类号: (Networks and genealogical trees)

89.75.Hc

89.75.-k (Complex systems) 64.60.aq (Networks)

. [J]. 中国物理B, 2020, 29(12): 128901-.

Jialin He(何嘉林), Dongmei Li(李冬梅), and Yuexi Liu(刘阅希). Modularity-based representation learning for networks[J]. Chin. Phys. B, 2020, 29(12): 128901-.

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