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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Veröffentlicht in:	2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) S. 80 - 89
1. Verfasser:	Chehreghani, Mostafa Haghir
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2016
Schlagworte:	Algorithm design and analysis approximate algorithms Approximation algorithms Clustering algorithms Correlation Eigenvalues and eigenfunctions Graphs large network analysis Probability distribution Time complexity triangle counting
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