Paper Info
Title
Inferring User Profiles in Online Social Networks Based on Convolutional Neural Network.
Abstract
We propose a novel method to infer missing attributes (e.g., occupation, gender, and location) of online social network users, which is an important problem in social network analysis. Existing works generally utilize classification algorithms or label propagation methods to solve this problem. However, these works had to train a specific model for inferring one kind of missing attributes, which achieve limited precision rates in inferring multi-value attributes. To address above challenges, we proposed a convolutional neural network architecture to infer users' all missing attributes based on one trained model. And it's novel that we represent the input matrix using features of target user and his neighbors, including their explicit attributes and behaviors which are available in online social networks. In the experiments, we used a real-word large scale dataset with 220,000 users, and results demonstrated the effectiveness of our method and the importance of social links in attribute inference. Especially, our work achieved a 76.28% precision in the occupation inference task which improved upon the state of the art.
Year
DOI
Venue
2017
10.1007/978-3-319-63558-3_23
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
User attributes mining,Deep neural network,Social network analysis
Data mining,Architecture,Social network,Convolutional neural network,Inference,Label propagation,Computer science,Social network analysis,Artificial intelligence,Deep learning,Statistical classification,Machine learning
Conference
Volume
ISSN
Citations 
10412
0302-9743
1
PageRank 
References 
Authors
0.38
16
6
Name
Order
Citations
PageRank
XiaoXue Li1225.58
Ya-nan Cao213119.42
Yanmin Shang365.92
Yanbing Liu41912.33
Jianlong Tan513222.14
Li Guo610210.23