Abstract
Inferring users’ social role on a mobile communication network is of significance for various applications, such as financial fraud detection, viral marketing, and target promotions. Different with the social network, which has lots of user generated contents (UGC) including texts, pictures, and videos, considering the privacy issues, mobile communication network only contains the communication pattern data, such as message frequency and phone call frequency as well as duration. Moreover, the profile data of mobile users is always noisy, ambiguous, and sparse, which makes the task more challenging. In this paper, we use the graph embedding methods as a feature extractor and then combine it with the hand-crafted structure-related features in a feed-forward neural network. Different with previous embedding methods, we consider the label info while sampling the context. To handle the noisy and sparsity challenge, we further project the generated embedding onto a much smaller subspace. Through our experiments, we can increase the precision by up to 10% even with a huge portion of noisy and sparse labeled data.
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This work was supported by Natural Science Foundation of China (No. 61170003).
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Zhang, J., Chen, Y., Hong, S., Li, H. (2017). REBUILD: Graph Embedding Based Method for User Social Role Identity on Mobile Communication Network. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_33
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DOI: https://doi.org/10.1007/978-3-319-61845-6_33
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