Chonggang Song

Bio: Chonggang Song is an academic researcher from Tencent. The author has contributed to research in topics: Social network. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

LoCEC: Local Community-based Edge Classification in Large Online Social Networks

Abstract: Relationships in online social networks often imply social connections in real life. 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.

LoCEC: Local Community-based Edge Classification in Large Online Social Networks

Abstract: 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.

Graph Auto-Encoders for Learning Edge Representations

Abstract: Graphs evolved as very effective representations of different types of data including social networks, biological data or textual documents. In the past years, significant efforts have been devoted to methods that learn vector representations of nodes or of entire graphs. But edges, representing interactions between nodes, have attracted less attention. Surprisingly, there are only a few studies that focus on generating edge representations or deal with edge-related tasks such as the problem of edge classification. In this paper, we propose a new model (in the form of an auto-encoder) to learn edge embeddings in (un)directed graphs. The encoder corresponds to a graph neural network followed by an aggregation function, while a multi-layer perceptron serves as our decoder. We empirically evaluate our approach in two different tasks, namely edge classification and link prediction. In the first task, the proposed model outperforms the baselines, while in the second task, it achieves results that are comparable to the state-of-the-art.