SuperGraph Visualization

TL;DR

This paper introduces GMine, a scalable graph visualization system that uses hierarchical graph partitions to enable interactive exploration of large networks with hundreds of thousands of nodes and edges.

Contribution

It presents a novel hierarchical approach to graph visualization that improves scalability and analytical capabilities for large graphs.

Findings

Enables interactive visualization of large graphs with hundreds of thousands of nodes.

Supports analysis of patterns, outliers, and communities within large networks.

Extends existing tools with a hierarchical framework for better scalability.

Abstract

Given a large social or computer network, how can we visualize it, find patterns, outliers, communities? Although several graph visualization tools exist, they cannot handle large graphs with hundred thousand nodes and possibly million edges. Such graphs bring two challenges: interactive visualization demands prohibitive processing power and, even if we could interactively update the visualization, the user would be overwhelmed by the excessive number of graphical items. To cope with this problem, we propose a formal innovation on the use of graph hierarchies that leads to GMine system. GMine promotes scalability using a hierarchy of graph partitions, promotes concomitant presentation for the graph hierarchy and for the original graph, and extends analytical possibilities with the integration of the graph partitions in an interactive environment.