QQ客服 官方微博 反馈留言

	A Feature-Based Approach for the Redefined Link Prediction Problem in Signed Networks
	Li, Xiaoming1; Fang, Hui2; Zhang, Jie1
	2017
关键词	Signed social network Link prediction No-relation
卷号	10604
DOI	10.1007/978-3-319-69179-4_12
页码	165-179
英文摘要	Link prediction is an important research issue in social networks, which can be applied in many areas, such as trust-aware business applications and viral marketing campaigns. With the rise of signed networks, the link prediction problem becomes more complex and challenging as it introduces negative relations among users. Instead of predicting future relation for a pair of users, however, the current research focuses on distinguishing whether a certain link is positive or negative, on the premise of the link existence. The situation that two users do not have relation (i.e., no-relation) is also not considered, which actually is the most common case in reality. In this paper, we redefine the link prediction problem in signed social networks by also considering "no-relation" as a future status of a node pair. To understand the underlying mechanism of link formation in signed networks, we propose a feature framework on the basis of a thorough exploration of potential features for the newly identified problem. We find that features derived from social theories can well distinguish these three social statuses. Grounded on the feature framework, we adopt a multiclass classification model to leverage all the features, and experiments show that our method outperforms the state-of-the-art methods.
会议录出版者	SPRINGER INTERNATIONAL PUBLISHING AG
会议录出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000449973300012
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/3350]
专题	上海财经大学
作者单位	1.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore; 2.Shanghai Univ Finance & Econ, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaoming,Fang, Hui,Zhang, Jie. A Feature-Based Approach for the Redefined Link Prediction Problem in Signed Networks[C]. 见:.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。