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Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs.

Jinghan Meng¹, Yi-Cheng Tu¹

¹Department of Computer Science & Engineering, University of South Florida.

Proceedings. ACM-SIGMOD International Conference on Management of Data

|March 1, 2024

Summary

This study introduces a new hypergraph framework for graph mining, unifying existing support measures and proposing novel minimum instance (MI) and minimum vertex cover (MVC) measures for more effective frequent pattern discovery in single graphs.

Keywords:

Data mining graph mining hypergraph support measures

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Area of Science:

Computer Science
Data Mining
Graph Theory

Background:

Graph databases are increasingly popular for information representation.
Frequent pattern mining in single graphs faces challenges with support measures and search schemes.

Purpose of the Study:

To propose a novel framework for constructing support measures in single-graph mining.
To introduce new support measures that combine advantages of existing methods.
To unify various support measures within a single theoretical framework.

Main Methods:

Developed a framework based on occurrence/instance hypergraphs.
Introduced Minimum Instance (MI) and Minimum Vertex Cover (MVC) support measures.
Provided polynomial-time relaxations and bounding theorems for hypergraph-based measures.

Main Results:

The proposed framework unifies existing and new support measures.
The MI measure is linear-time computable and approximates instance counts.
The MVC measure, though NP-hard, is approximable in polynomial time.

Conclusions:

The hypergraph-based framework offers a unified and flexible approach to defining and analyzing support measures in graph mining.
The new MI and MVC measures provide effective alternatives for frequent pattern discovery.