A Regularized Graph Layout Framework for Dynamic Network Visualization
Kevin S. Xu, Mark Kliger, and Alfred O. Hero III

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
