A new method for quantifying network cyclic structure to improve community detection
From MaRDI portal
Publication:2143282
DOI10.1016/j.physa.2020.125116OpenAlexW3081306725MaRDI QIDQ2143282
Heman Shakeri, Pietro Poggi-Corradini, Michael J. Higgins, Behnaz Moradi-Jamei
Publication date: 31 May 2022
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1805.07484
Cites Work
- Unnamed Item
- Graph partitioning models for parallel computing
- Consistency of community detection in networks under degree-corrected stochastic block models
- Non-backtracking random walk
- Enhancing community detection using a network weighting strategy
- A faster algorithm for betweenness centrality*
- The distribution of path lengths of self avoiding walks on Erdős–Rényi networks
- A First Look at Rigorous Probability Theory
- NON-BACKTRACKING RANDOM WALKS MIX FASTER
- The Structure and Function of Complex Networks
- Community structure in social and biological networks
- Fast unfolding of communities in large networks
- The distribution of first hitting times of non-backtracking random walks on Erdős–Rényi networks
- Probability Inequalities for Sums of Bounded Random Variables
This page was built for publication: A new method for quantifying network cyclic structure to improve community detection