Accurate Online Social Network User Profiling

被引：2

作者：

Dougnon, Raissa Yapan ^{[1
]}

Fournier-Viger, Philippe ^{[1
]}

Lin, Jerry Chun-Wei ^{[2
]}

Nkambou, Roger ^{[3
]}

机构：

[1] Univ Moncton, Dept Comp Sci, Moncton, NB E1A 3E9, Canada

[2] Harbin Inst Technol, Shenzhen Grad Sch, Sch Comp Sci & Technol, Shenzhen, Peoples R China

[3] Univ Quebec, Dept Comp Sci, Montreal, PQ H3C 3P8, Canada

来源：

KI 2015: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2015年 / 9324卷

关键词：

Social networks; Inference; User profiles; Partial graph;

D O I：

10.1007/978-3-319-24489-1_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present PGPI+ (Partial Graph Profile Inference+) an improved algorithm for user profiling in online social networks. PGPI+ infers user profiles under the constraint of a partial social graph using rich information about users (e.g. group memberships, views and likes) and handles nominal and numeric attributes. Experimental results with 20,000 user profiles from the Pokec social network shows that PGPI+ predicts user profiles with considerably more accuracy and by accessing a smaller part of the social graph than five state-of-the-art algorithms.

引用

页码：264 / 270

页数：7

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