# 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…

## 171 Citations

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

- Computer ScienceSci. Program.
- 2019

This paper proposes a method named as “biCNEQ” (bipartite network communities number estimation based on quality of filtering coefficient), which ensures that communities are all pure type, for estimating the number of communities in a bipartitenetwork.

### Latent geometry of bipartite networks

- Computer SciencePhysical review. E
- 2017

This model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartites clustering, and proposes an efficient method to infer the latent pairwise distances between nodes.

### Efficient Detection of Communities in Biological Bipartite Networks

- Computer SciencebioRxiv
- 2017

Experimental results show that the biLouvain algorithm identifies communities that have a comparable or better quality than existing methods, while significantly reducing the time-to-solution between one and four orders of magnitude.

### Detecting Communities in Biological Bipartite Networks

- Computer ScienceBCB
- 2016

Experimental results show that the biLouvain algorithm identifies communities that have a comparable or better quality (bipartite modularity) than existing methods, while significantly reducing the time-to-solution between one and three orders of magnitude.

### Degree distributions of bipartite networks and their projections

- Computer SciencePhysical review. E
- 2018

This work uses the formalism of generating functions to prove that when projecting a bipartite network onto a particular set of nodes, the degree distribution for the resulting one-mode network follows the distribution of the nodes being projected on to, but only so long as the degree distributions for the opposite set of node does not have a heavier tail.

### A simple bipartite graph projection model for clustering in networks

- Computer ScienceArXiv
- 2020

A random graph model is proposed and analyzed to study what properties the authors can expect from the projection step, and it is shown that common network properties such as sparsity, heavy-tailed degree distributions, local clustering at nodes, the inverse relationship between node degree, and global transitivity can be explained and analyzed through this simple model.

### Mapping Flows in Bipartite Networks

- Computer SciencePhysical review. E
- 2020

The community landscape of bipartite real-world networks from no node- type information to full node-type information is explored and it is found that using node types at a higher rate generally leads to deeper community hierarchies and a higher resolution.

### A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks

- Computer ScienceSci. Program.
- 2019

A projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks and shows that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

### Community Detection in Networks with Node Covariates

- Computer Science
- 2017

This dissertation proposes a model-based approach which allows for matched communities in the bipartite setting, in addition to node covariates with information about the matching, and proposes a unified affinity matrix (USim) to leverage the node covariate information that can be used in unipartite networks (directed and undirected).

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