Efficiently inferring community structure in bipartite networks

@article{Larremore2014EfficientlyIC,
  title={Efficiently inferring community structure in bipartite networks},
  author={Daniel B. Larremore and Aaron Clauset and Abigail Z. Jacobs},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2014},
  volume={90 1},
  pages={
          012805
        }
}
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 64 CITATIONS, ESTIMATED 100% COVERAGE

Efficient Detection of Communities in Biological Bipartite Networks

  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • 2019
VIEW 9 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Imbalanced classification in sparse and large behaviour datasets

  • Data Mining and Knowledge Discovery
  • 2017
VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

  • Scientific Programming
  • 2019
VIEW 7 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Clustering Theory and Data Driven Health Care Strategies

VIEW 10 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

A Memetic Algorithm for Community Detection in Bipartite Networks

VIEW 3 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2014
2019

CITATION STATISTICS

  • 8 Highly Influenced Citations

  • Averaged 10 Citations per year from 2017 through 2019