A survey on visualization approaches for exploring association relationships in graph data

Exploring relationships in complex datasets is one of the challenges in today's big data era. The graph-based visualization approach, which integrates the advantages of graph analysis theory and visualization technologies and combines machine and human intelligence, has become an effective means for analyzing various relationships in complex datasets. In this paper, we first introduce a graph-based visual analytics model for associated data. Then, we summarize seven typical visualization methods for associated data according to their layout features, including their node-link diagram, adjacency matrix, hypergraph, flow diagram, graphs with geospatial information, multi-attribute graph, and space-filling diagram and discuss their advantages and disadvantages. We describe current graph simplification and interaction techniques, including graph filtering, node clustering, edge bundling, graph data dimension reduction, and topology-based graph transformation. Finally, we discuss the potential challenges and developmental trends of the research direction.