A Framework for Description and Analysis of Sampling-Based Approximate Triangle Counting Algorithms

Counting the number of triangles in a large graph has many important applications in network analysis. Several frequently computed metrics such as the clustering coefficient and the transitivity ratio need to count the number of triangles. In this paper, we present a randomized framework for express...

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Bibliographic Details

Published in:	2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) pp. 80 - 89
Main Author:	Chehreghani, Mostafa Haghir
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.10.2016
Subjects:	Algorithm design and analysis approximate algorithms Approximation algorithms Clustering algorithms Correlation Eigenvalues and eigenfunctions Graphs large network analysis Probability distribution Time complexity triangle counting
Online Access:	Get full text
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