Preface to the Special Issue on Graph Data Management in Online Social Networks

We are delighted to present this special issue of World Wide Web on Graph Data Management in Online Social Networks. Recent years have witnessed the explosion of graph data generated from a wide range of enterprises and applications at an unprecedented way. Graph data management, which concerns techniques in modelling, storing, querying, and learning graph data has been found particular useful in online social network (OSN) analysis, such as expert finding, social community mining and social position detection. The purpose of this special issue is to bring together some recent and significant results in analysing OSNs with novel graph data management techniques in a timely fashion. The guest editors selected 11 contributions that covers varying topics within this theme, ranging from graph-based representations to learning models, from graph-based query processing to analysis. Many problems in this area share common methods including graph data structures and deep learning models. Li et al. in “Semi-supervised Clustering with Deep Metric Learning and Graph Embedding” propose a novel semi-supervised clustering approach based on deep metric learning and graph embedding, which enhances the robustness of metric learning network and promotes the accuracy of clustering. World Wide Web https://doi.org/10.1007/s11280-019-00771-0