Large Graph Analysis in the GMine System

TL;DR

The paper introduces GMine, a system for interactive analysis of large, tree-like graphs that combines hierarchical representation and summarization to enable efficient exploration and pattern detection.

Contribution

It presents a novel framework with hierarchical graph representation and summarization techniques, improving scalability and interpretability for large graph analysis.

Findings

Efficient tracing of graph connections with sublinear complexity.

Interactive exploration of large graphs at global and local levels.

Successful implementation of GMine for large graph investigation.

Abstract

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally.