StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices

TL;DR

StructMatrix is a scalable visualization method that combines structure detection and dense matrices to reveal macro patterns in large graphs, aiding insights and decision-making.

Contribution

It introduces a novel scalable approach integrating structure detection with matrix visualization for large-scale graph analysis.

Findings

Revealed domain-specific macro patterns in large real-world graphs

Demonstrated effectiveness on graphs with up to one million nodes

Uncovered insights not previously documented in literature

Abstract

Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a methodology aimed at high-scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, large-scale graphs with up to one million nodes and millions of edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains; our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.