Visual Inspection of Multivariate Graphs

TLDR

Graph visualization techniques typically emphasize graph structure and lack support for node attributes and edge labels. This work introduces a data‑centric technique that lets users detect relations and patterns based on node and edge attributes. The method clusters nodes and edges by associated data, allows interactive inspection and querying through direct manipulation, and is illustrated on transition graphs modeling real‑world systems. The approach successfully answers questions such as which edge types are activated by specific node attributes and how to reach particular node types, and its validation against current practice confirms its effectiveness.

Abstract

Abstract Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real‐world systems.