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Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics.

Antonino Aparo1, Vincenzo Bonnici1, Giovanni Micale2

  • 1Department of Computer Science, University of Verona, Verona, Italy.

Interdisciplinary Sciences, Computational Life Sciences
|February 22, 2019
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This study evaluates subgraph isomorphism algorithms for scientific applications. Surprisingly, pattern-based heuristics proved most efficient among leading methods for solving this NP-complete problem.

Keywords:
Networks biologySearch strategySubgraph isomorphism

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Area of Science:

  • Computer Science
  • Graph Theory
  • Computational Complexity

Background:

  • The subgraph isomorphism problem is crucial for many scientific applications.
  • This problem involves finding structurally equivalent subgraphs within a larger target graph.
  • As an NP-complete problem, subgraph isomorphism necessitates heuristic approaches for efficient solutions.

Purpose of the Study:

  • To evaluate the computational behavior of various subgraph isomorphism methods.
  • To assess algorithm performance across diverse synthetic and real-world graph datasets.

Main Methods:

  • Experimental evaluation of leading subgraph isomorphism algorithms.
  • Analysis of computational performance on a wide range of graph types.

Main Results:

  • The study compared the efficiency of different subgraph isomorphism heuristics.
  • Surprisingly, heuristics relying solely on pattern graphs demonstrated superior efficiency.
  • This finding emerged from experiments on both synthetic and real-world graph data.

Conclusions:

  • Pattern-based heuristics are highly efficient for solving the subgraph isomorphism problem.
  • These findings offer practical insights for selecting algorithms in scientific applications.
  • The research highlights the effectiveness of focused heuristics in tackling computationally intensive graph problems.