Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs

Nowadays there is a great interest in the visualization of property graphs to make their navigation, inspection, and visual analysis easier. However, property graphs can be quite large and their rendering on web browsers can lead to a dark cloud of points that is difficult to visually explore. With the aim of reducing the size of the visualized graph, several approaches have been proposed for substituting clusters of related vertices with aggregated meta-nodes and introducing meta-edges among them, but they usually consider the graph in main-memory and do not adopt efficient data structures for extracting parts of it from the disk. The purpose of this paper is to optimize the preparation of the graph to be visualized according to a certain resolution level by introducing refined data structures and specifically tailored algorithms. By means of them, the rendering time is reduced when changing the current visualization through zoom-in, zoom-out, and related operations. Starting from a cluster hierarchy that represents the possible aggregations of graph nodes, in the paper we characterize a visualization according to a horizontal slice of the hierarchy and propose indexing structures and incremental algorithms for quickly passing to a new visualization with minimal changes of the current one. In this process, we ensure a consistent and efficient aggregation of addictive properties associated with nodes and edges. An extensive experimental analysis has been conducted to assess the quality of the proposed solution.