Abstract

Graph pattern mining (GPM) is a key building block in diverse applications, including bioinformatics, chemical engineering, social network analysis, recommender systems and security. Existing GPM frameworks either provide high-level interfaces for productivity at the cost of expressiveness or provide low-level interfaces that can express a wide variety of GPM algorithms at the cost of increased programming complexity. Moreover, existing systems lack the flexibility to explore combinations of optimizations to achieve performance competitive with hand-optimized applications.

Highlights

  • Graph pattern mining (GPM) problems exist in many application domains [5, 16, 18, 22]

  • We show that existing optimizations in the literature applied to specific problems/applications can be applied more generally to other GPM problems

  • If v1 and v2 occur in a vertex-induced subgraph, e occurs in the subgraph as well; in an edge-induced subgraph, edge e will be present only if it is in the given edge set F

Read more

Summary

Introduction

Graph pattern mining (GPM) problems exist in many application domains [5, 16, 18, 22]. One example is motif counting [7, 25, 43], which counts the number of occurrences of certain structural patterns in a given graph (Fig. 1). Given a vertex set W ⊆ V , the vertex-induced subgraph is the graph G whose (1) vertex set is W and whose (2) edge set contains the edges in E whose endpoints are in W. Given an edge set F ⊆ E, the edge-induced subgraph is the graph G whose (1) edge set is F and whose (2) vertex set contains the endpoints in V of the edges in F. When f is a mapping of a graph onto itself, i.e., G1 and G2 are the same graph, G1 and G2 are automorphic, i.e. G1 G2

Methods
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.