Name
Title | ||
|---|---|---|
Predicting the attributes of social network users using a graph-based machine learning method |
Abstract | ||
|---|---|---|
Attribute information from social network users can be used as a basis for grouping users, sharing content, and recommending friends. However, in practice, not all users provide their attributes. In this paper, we try to use information from both the graph structure of the social network and the known attributes of users to predict the unknown attributes of users. Considering the topological structure of a social network and the characteristics of users’ data, we select a graph-based semi-supervised learning algorithm to predict users’ attributes. We design different strategies for computing the relational weights between users. The experimental results on real-world data from Renren demonstrate that the semi-supervised learning method is more suitable for predicting users’ attributes compared with the supervised learning models, and our strategies for computing the relational weights between users are effective. We also analyze the effect of different social relations on predicting users’ attributes. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.comcom.2015.07.007 | Computer Communications |
Keywords | Field | DocType |
Social network analysis,Data mining,Social network privacy,Semi-supervised learning,Information inference | Social relation,Graph,Semi-supervised learning,Social network,Computer science,Social network analysis,Supervised learning,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
73 | PA | 0140-3664 |
Citations | PageRank | References |
6 | 0.51 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxin Ding | 1 | 237 | 21.52 |
| Yan Shengli | 2 | 10 | 1.65 |
| Yibin Zhang | 3 | 29 | 4.70 |
| Dai Wei | 4 | 13 | 2.70 |
| Dong Li | 5 | 115 | 48.55 |