Can Embedding Solve Scalability Issues for Mixed-Data Graph Clustering?

摘要

It is widely accepted that the field of Data Analytics has entered into the era of Big Data. In particular, it has to deal with so-called Big Graph Data, which is the focus of this paper. Graph Data is present in many fields, such as Social Networks, Biological Networks, Computer Networks, and so on. It is recognized that data analysts benefit from interactive real time data exploration techniques such as clustering and zoom capabilities on the clusters. However, although clustering is one of the key aspects of graph data analysis, there is a lack of scalable graph clustering algorithms which would support interactive techniques. This paper presents an approach based on combining graph clustering and graph coordinate system embedding, and which shows promising results through initial experiments. Our approach also incorporates both structural and attribute information, which can lead to a more meaningful clustering.