全球变化知识资源中心

Knowledge Resources Center for Global Change


Email:
Passwd
验证:
	换一张
Have you forgotten your password? Stay signed in Log In

globalchange > 过去全球变化的重建

DOI:	10.1371/journal.pone.0110088
论文题名:	Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints
作者:	Jianjun Cheng; Mingwei Leng; Longjie Li; Hanhai Zhou; Xiaoyun Chen
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2014
发表日期:	2014-10-17
卷:	9, 期:10
语种:	英语
英文关键词:	Algorithms ; Social networks ; Random walk ; Dolphins ; Scientists ; Machine learning algorithms ; Graphs ; Protein interaction networks
英文摘要:	Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0110088&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/18526
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

Files in This Item:	Download All

File Name/ File Size	Content Type	Version	Access	License
journal.pone.0110088.PDF（2650KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China

Recommended Citation:

Jianjun Cheng,Mingwei Leng,Longjie Li,et al. Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints[J]. PLOS ONE,2014-01-01,9(10)

Service
	Recommend this item
	Sava as my favorate item
	Show this item's statistics
	Export Endnote File
Google Scholar
	Similar articles in Google Scholar
	[Jianjun Cheng]'s Articles
	[Mingwei Leng]'s Articles
	[Longjie Li]'s Articles
百度学术
	Similar articles in Baidu Scholar
	[Jianjun Cheng]'s Articles
	[Mingwei Leng]'s Articles
	[Longjie Li]'s Articles
CSDL cross search
	Similar articles in CSDL Cross Search
	[Jianjun Cheng]‘s Articles
	[Mingwei Leng]‘s Articles
	[Longjie Li]‘s Articles
Related Copyright Policies
Null
收藏/分享