Towards an Efficient Discovery of the Topological Representative Subgraphs

TL;DR

This paper introduces a novel method for efficiently discovering the top-k topological representative subgraphs from frequent subgraph sets, reducing redundancy and capturing hidden structural similarities to improve graph analysis.

Contribution

The paper proposes a new approach to identify topologically representative subgraphs that detects hidden similarities and is easily extendable with user-defined attributes.

Findings

The approach is fast and scalable on real and synthetic datasets.

It effectively reduces the number of subgraphs while preserving structural diversity.

It detects hidden structural similarities like density and diameter.

Abstract

With the emergence of graph databases, the task of frequent subgraph discovery has been extensively addressed. Although the proposed approaches in the literature have made this task feasible, the number of discovered frequent subgraphs is still very high to be efficiently used in any further exploration. Feature selection for graph data is a way to reduce the high number of frequent subgraphs based on exact or approximate structural similarity. However, current structural similarity strategies are not efficient enough in many real-world applications, besides, the combinatorial nature of graphs makes it computationally very costly. In order to select a smaller yet structurally irredundant set of subgraphs, we propose a novel approach that mines the top-k topological representative subgraphs among the frequent ones. Our approach allows detecting hidden structural similarities that existing approaches are unable to detect such as the density or the diameter of the subgraph. In addition, it can be easily extended using any user defined structural or topological attributes depending on the sought properties. Empirical studies on real and synthetic graph datasets show that our approach is fast and scalable.