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A linear delay algorithm for enumerating all connected induced subgraphs.

Mohammed Alokshiya1, Saeed Salem2, Fidaa Abed3

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We developed a faster algorithm for finding connected induced subgraphs and maximal cohesive subgraphs in graph data. This graph mining technique improves biological network analysis and uncovers significant interactions.

Keywords:
Biological networksReverse searchSubgraph enumeration

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Area of Science:

  • Graph theory
  • Network analysis
  • Computational biology

Background:

  • Biological and social data are increasingly represented as graphs.
  • Graph learning techniques identify meaningful biological subnetworks.
  • Enumerating pattern space is crucial for these algorithms.

Purpose of the Study:

  • To propose an efficient algorithm for enumerating all connected induced subgraphs.
  • To develop an algorithm for mining maximal cohesive subgraphs integrating vertex attributes.
  • To introduce pruning techniques for efficient subgraph enumeration.

Main Methods:

  • Algorithm for enumerating connected induced subgraphs.
  • Algorithm for mining maximal cohesive subgraphs with attribute integration.
  • Two pruning techniques to optimize search space.

Main Results:

  • The proposed algorithm is significantly faster than existing approaches for subgraph enumeration.
  • The approach is orders of magnitude faster on dense graphs.
  • Experiments demonstrate effectiveness on synthetic and real-world graphs.

Conclusions:

  • The algorithm efficiently enumerates connected induced subgraphs and mines maximal cohesive subgraphs.
  • Pruning techniques enhance computational efficiency.
  • Applied to protein-protein interaction networks, it reveals biologically relevant subnetworks.