A Regularized Graph Layout Framework for Dynamic Network Visualization

TL;DR

This paper introduces a regularized framework for dynamic network visualization that enhances stability and interpretability by incorporating grouping and temporal penalties into layout algorithms, effectively preserving the mental map over time.

Contribution

It proposes a novel framework that augments static graph layout algorithms with regularization terms for dynamic visualization, including two specific algorithms: DMDS and DGLL.

Findings

Regularization improves layout stability over time

Grouping and temporal penalties enhance interpretability

Algorithms effectively visualize evolving networks

Abstract

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.