Juniper: A Tree+Table Approach to Multivariate Graph Visualization

TL;DR

Juniper introduces a scalable tree+table visualization method for multivariate graphs, combining node-link and adjacency matrix views to facilitate analysis of large, complex networks.

Contribution

The paper presents a novel hybrid visualization technique that integrates tree layouts with tabular and matrix views for multivariate graph analysis.

Findings

Enables dynamic growth and restructuring of the tree visualization.

Supports attribute comparison through integrated table and matrix views.

Demonstrates effectiveness in diverse multivariate network scenarios.

Abstract

Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.