Matched bipartite block model with covariates

03/15/2017
by   Zahra S. Razaee, et al.
0

Community detection or clustering is a fundamental task in the analysis of network data. Many real networks have a bipartite structure which makes community detection challenging. In this paper, we consider a model which allows for matched communities in the bipartite setting, in addition to node covariates with information about the matching. We derive a simple fast algorithm for fitting the model based on variational inference ideas and show its effectiveness on both simulated and real data. A variation of the model to allow for degree-correction is also considered, in addition to a novel approach to fitting such degree-corrected models.

READ FULL TEXT

page 20

page 24

research
07/10/2018

Pairwise Covariates-adjusted Block Model for Community Detection

One of the most fundamental problems in network study is community detec...
research
01/22/2020

Community Detection in Bipartite Networks with Stochastic Blockmodels

In bipartite networks, community structures are restricted to being disa...
research
07/24/2016

Community Detection in Degree-Corrected Block Models

Community detection is a central problem of network data analysis. Given...
research
03/12/2014

Efficiently inferring community structure in bipartite networks

Bipartite networks are a common type of network data in which there are ...
research
05/27/2021

Comparing Models for Extracting the Backbone of Bipartite Projections

Projections of bipartite or two-mode networks capture co-occurrences, an...
research
05/05/2017

Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting

Latent space models are effective tools for statistical modeling and exp...
research
06/27/2023

Network-Adjusted Covariates for Community Detection

Community detection is a crucial task in network analysis that can be si...

Please sign up or login with your details

Forgot password? Click here to reset