Context Specificity Matters: Profile Attributes Prediction for Social Network Users

Analysis of social network user profiles plays an important role in various applications such as recommender systems and marketing services. In this paper we consider the task of profile prediction given a sample of a social graph. We introduce a new node property named context specificity, which allows to enhance node attribute prediction methods by emphasizing more useful nodes and edges. Particularly, we show how to apply it to several existing methods: label propagation, classification based on node embeddings, graph convolutional neural network. Our experiments prove that context specificity feature improves methods quality in most considered tasks. Additionally, we suggest a new supervised method based on attribute values distributions that is competitive with state of the art approaches. We also describe and share two new datasets, representing a sample of VK social network.

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