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.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China grants (NO. 61403369, NO. 61602466), the National Key Research and Development program (No. 2016YFB0801304).
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Li, X., Cao, Y., Shang, Y., Liu, Y., Tan, J., Guo, L. (2017). Inferring User Profiles in Online Social Networks Based on Convolutional Neural Network. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_23
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DOI: https://doi.org/10.1007/978-3-319-63558-3_23
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