Convex Relaxation for Community Detection with Covariates

07/10/2016
by   Bowei Yan, et al.
0

Community detection in networks is an important problem in many applied areas. In this paper, we investigate this in the presence of node covariates. Recently, an emerging body of theoretical work has been focused on leveraging information from both the edges in the network and the node covariates to infer community memberships. However, so far the role of the network and that of the covariates have not been examined closely. In essence, in most parameter regimes, one of the sources of information provides enough information to infer the hidden cluster labels, thereby making the other source redundant. To our knowledge, this is the first work which shows that when the network and the covariates carry "orthogonal" pieces of information about the cluster memberships, one can get improved clustering accuracy by using them both, even if each of them fails individually.

READ FULL TEXT
research
06/27/2023

Network-Adjusted Covariates for Community Detection

Community detection is a crucial task in network analysis that can be si...
research
03/04/2022

Bayesian community detection for networks with covariates

The increasing prevalence of network data in a vast variety of fields an...
research
07/30/2022

Covariate-Assisted Community Detection on Sparse Networks

Community detection is an important problem when processing network data...
research
07/23/2018

Contextual Stochastic Block Models

We provide the first information theoretic tight analysis for inference ...
research
06/08/2016

On clustering network-valued data

Community detection, which focuses on clustering nodes or detecting comm...
research
09/30/2018

Convex Relaxation Methods for Community Detection

This paper surveys recent theoretical advances in convex optimization ap...
research
04/18/2022

A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates

In this study, we examine a clustering problem in which the covariates o...

Please sign up or login with your details

Forgot password? Click here to reset