Clustering on Attributed Graphs: From Single-view to Multi-view

Attributed graphs with both topological information and node information have prevalent applications in the real world, including recommendation systems, biological networks, community analysis, and so on. Recently, with rapid development of information gathering and extraction technology, the sources of data become more extensive and multi-view data attracts growing attention. Consequently, attributed graphs can be divided into two categories: single-view attributed graphs and multi-view attributed graphs. Compared with single-view attributed graphs, multi-view attributed graphs can provide more complementary information but also pose challenges to fusing information of multi-views. Moreover, attributed graph clustering aims to reveal the inherent community structure of the graph, which is widely applied in fraud detection, crime recognition, and recommendation systems. Recently, numerous methods based on various ideas and techniques have appeared to cluster attributed graphs, thus there is an urgent need to summarize related methods. To this end, we make a timely and comprehensive review of recent methods. Furthermore, we provide a novel standard according to fusion results to classify related methods into three categories: Fusion on adjacency matrix methods, Fusion on embedding methods, and Model-based methods. Moreover, to conduct a comprehensive evaluation of existing methods, this paper evaluates these advanced methods with sufficient experimental results and theoretical analysis. Finally, we analyze the challenges and open opportunities to promote the future development of this field.