Co-learning Graph Convolution Network for Mobile User Profiling

Mobile user profiling has drawn significant attentions from various disciplines. To deeply understand the mobile users, based on users’ application (app) text data in the smartphone, we propose a semi-supervised learning method to infer mobile user profiles or user demographic attributes. App text has the characteristics of short text length and no word order, which leads to the problems of sparse semantics and lack of context, etc. To address these problems, we build two heterogeneous graphs with different scale features for the corpus, using app name (word) and app installation text list (document) as the nodes of the graph, and using three rules to build edges. Thus, text classification is transformed into multi-graph node classification. Then, we propose a co-learning graph convolutional network (C-GCN) based on selective-scale attention (SS-Attention) to realize the extraction of spatial features of graphs. SS-Attention can enhance the representation learning of global important (word-level) nodes by splitting and fusing operations. Experimental results demonstrated that, without using pre-training embedding, C-GCN outperforms state-of-the-art models across real data.