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k-Clique counting on large scale-graphs: a survey.

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  • 1Computer Engineering, Izmir Institute of Technology, Izmir, Turkey.

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Summary
This summary is machine-generated.

This paper reviews algorithms for counting k-cliques (k>3) in large graphs, addressing a gap in existing research. It analyzes exact and approximation methods to guide future k-clique counting research.

Keywords:
Approximate clique countingClique countingExact clique countingGraph miningGraphlet countingLocal graphlet countingMaximal clique countingNetwork motifsParallel clique countingSubgraph enumeration

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Area of Science:

  • Graph mining and network analysis
  • Computational complexity and algorithms

Background:

  • Clique counting is vital for network analysis across domains like social networks, fraud detection, and recommendation systems.
  • Existing research extensively covers triangle (3-clique) counting but lacks comprehensive reviews for k-clique counting where k > 3.
  • The combinatorial explosion inherent in clique counting poses significant algorithmic challenges for large datasets.

Purpose of the Study:

  • To address the research gap by reviewing algorithms for k-clique counting (k>3).
  • To provide a systematic analysis and comparison of exact and approximation techniques for k-clique counting.
  • To present a taxonomy of k-clique counting methodologies, including parallelization strategies.

Main Methods:

  • Systematic literature review of k-clique counting algorithms.
  • Comparative analysis of exact and approximation algorithms.
  • Taxonomic classification of methodologies based on approach and parallelization.

Main Results:

  • Identified and categorized various k-clique counting algorithms for k>3.
  • Highlighted the advantages, disadvantages, and contextual suitability of different exact and approximation techniques.
  • Presented a structured overview of existing k-clique counting strategies.

Conclusions:

  • The review enhances understanding of k-clique counting challenges and solutions.
  • The findings guide researchers in selecting appropriate algorithms for large-scale graph analysis.
  • This work aims to stimulate future research in efficient k-clique counting methods.