Clustering with Lattices in the Analysis of Graph Patterns

TL;DR

This paper introduces Lattice2SAR, a framework that leverages lattice structures from graph mining to improve clustering and analysis of frequent subgraph patterns, especially in molecular datasets.

Contribution

The work presents a novel method for integrating lattice information into clustering to efficiently analyze frequent subgraph patterns in molecules.

Findings

Reduces data access by using lattice information for clustering.

Enables direct identification of patterns in molecules.

Improves analysis efficiency of frequent subgraph data.

Abstract

Mining frequent subgraphs is an area of research where we have a given set of graphs (each graph can be seen as a transaction), and we search for (connected) subgraphs contained in many of these graphs. In this work we will discuss techniques used in our framework Lattice2SAR for mining and analysing frequent subgraph data and their corresponding lattice information. Lattice information is provided by the graph mining algorithm gSpan; it contains all supergraph-subgraph relations of the frequent subgraph patterns -- and their supports. Lattice2SAR is in particular used in the analysis of frequent graph patterns where the graphs are molecules and the frequent subgraphs are fragments. In the analysis of fragments one is interested in the molecules where patterns occur. This data can be very extensive and in this paper we focus on a technique of making it better available by using the lattice information in our clustering. Now we can reduce the number of times the highly compressed occurrence data needs to be accessed by the user. The user does not have to browse all the occurrence data in search of patterns occurring in the same molecules. Instead one can directly see which frequent subgraphs are of interest.