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This study introduces a new framework for efficiently counting cycles and walks in directed graphs. The method utilizes matrix multiplication for faster graph analysis and improved distance queries.

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Area of Science:

  • Graph theory
  • Discrete mathematics
  • Computer science algorithms

Background:

  • Analyzing properties of unweighted, directed graphs is fundamental in computer science.
  • Efficiently counting cycles and walks is crucial for various graph algorithms.
  • Existing methods may not scale well for large graph structures.

Purpose of the Study:

  • To introduce a novel framework for counting cycles and walks in directed graphs.
  • To achieve matrix multiplication time complexity for these counting problems.
  • To enhance the efficiency of solving related graph problems like All-Nodes Shortest Cycles and All-Pairs All Walks.

Main Methods:

  • The framework is based on the fast decomposition into Frobenius normal form.
  • It leverages efficient Hankel matrix-vector multiplication.
  • The approach enables matrix multiplication time complexity for cycle and walk counting.

Main Results:

  • Successfully developed a framework for counting cycles and walks of a given length.
  • Achieved efficient solutions for All-Nodes Shortest Cycles and All-Pairs All Walks problems.
  • Demonstrated improvements in distance queries for unweighted graphs.

Conclusions:

  • The proposed framework offers a significant advancement in analyzing directed graphs.
  • The method provides efficient solutions for fundamental graph traversal and cycle detection problems.
  • This work contributes to the development of faster graph algorithms.