LoCEC: Local Community-based Edge Classification in Large Online Social Networks
This work addresses the challenge of relationship classification for applications such as social advertising and recommendation in large-scale social networks, but it is incremental as it builds on existing methods to handle sparsity and scalability.
The paper tackles the problem of classifying user relationships into real-world social connection types in large online social networks like WeChat, where relationship features and labels are sparse, by proposing the LoCEC framework, which achieves effectiveness and efficiency validated through experiments on a network with hundreds of billions of edges.
Relationships in online social networks often imply social connections in the real world. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of pre-labeled relationships or abundant interaction features on relationships. Unfortunately, both relationship feature data and label data are very sparse in real social platforms like WeChat, rendering existing methods inapplicable. In this paper, we present an in-depth analysis of WeChat relationships to identify the major challenges for the relationship classification task. To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types. LoCEC enforces a three-phase processing, namely local community detection, community classification and relationship classification, to address the sparsity issue of relationship features and relationship labels. Moreover, LoCEC is designed to handle large-scale networks by allowing parallel and distributed processing. We conduct extensive experiments on the real-world WeChat network with hundreds of billions of edges to validate the effectiveness and efficiency of LoCEC.