A Deep Generative Model for Reordering Adjacency Matrices

TL;DR

This paper introduces a deep generative model that learns a latent space of diverse adjacency matrix reorderings for graphs, facilitating easier visualization and analysis by users.

Contribution

It presents a novel machine learning approach to generate diverse matrix reorderings, improving graph visualization without relying solely on traditional algorithms.

Findings

Model successfully learns a latent space of reorderings

Generated reorderings are diverse and useful for visualization

User interface enables intuitive exploration of reorderings

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.