Online force-directed algorithms for visualization of dynamic graphs

TL;DR

This paper introduces five online force-directed algorithms designed for real-time visualization of dynamic graphs, effectively handling topology changes while maintaining force models, and outperforming static algorithms in key visual metrics.

Contribution

The paper presents novel online FD algorithms that adapt static force-directed methods for dynamic graphs, enabling efficient updates without full recomputation.

Findings

Online FD algorithms outperform static ones in edge crossings.

They maintain stable edge lengths during graph updates.

The algorithms effectively visualize evolving network structures.

Abstract

Force-directed (FD) algorithms can be used to explore relationships in social networks, visualize money markets, and analyze transaction networks. However, FD algorithms are mainly designed for visualizing static graphs in which the topology of the networks remains constant throughout the calculation. In contrast to static graphs, nodes and edges in dynamic graphs can be added or removed as time progresses. In these situations, existing FD algorithms do not scale well, since any changes in the topology will trigger these algorithms to completely restart the entire computation. To alleviate this problem, we propose a design and implementation of five online FD algorithms to visualize dynamic graphs while maintaining their native force models. The online FD algorithms developed in this paper are able to reuse the force models of existing FD algorithms without significant modifications. To evaluate the effectiveness of the proposed approach, online FD algorithms are compared against static FD algorithms for visualizing dynamic graphs. Experimental results show that among the five algorithms evaluated, the online FD algorithm achieves the best number of edge crossings and the standard deviation of edge lengths for visualizing dynamic graphs.