A Deep Generative Model for Reordering Adjacency Matrices
arXiv:2110.04971 · doi:10.1109/TVCG.2022.3153838
Abstract
Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal. However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. This paper presents a technique that enables users to effortlessly find a matrix reordering they want. Specifically, we design a generative model that learns a latent space of diverse matrix reorderings of the given graph. We also construct an intuitive user interface from the learned latent space by creating a map of various matrix reorderings. We demonstrate our approach through quantitative and qualitative evaluations of the generated reorderings and learned latent spaces. The results show that our model is capable of learning a latent space of diverse matrix reorderings. Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. This paper introduces a fundamentally new approach to matrix visualization of a graph, where a machine learning model learns to generate diverse matrix reorderings of a graph.
arXiv commentIEEE Transactions on Visualization and Computer Graphics
References in corpus (15)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Auto-Encoding Variational Bayes
- From Louvain to Leiden: guaranteeing well-connected communities
- Cooperative Game Theory Approaches for Network Partitioning
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Self-Attention Generative Adversarial Networks
- On the Convergence of Adam and Beyond
- Community Structure in Jazz
- GANSynth: Adversarial Neural Audio Synthesis
- Learning Latent Permutations with Gumbel-Sinkhorn Networks
- Adversarial Feature Matching for Text Generation
- What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
- Efficient Attention: Attention with Linear Complexities
- A Deep Generative Model for Graph Layout
- Stochastic Optimization of Sorting Networks via Continuous Relaxations