DeepAI AI Chat
Log In Sign Up

Active Community Detection: A Maximum Likelihood Approach

01/11/2018
by   Benjamin Mirabelli, et al.
nokia-bell-labs.com
0

We propose novel semi-supervised and active learning algorithms for the problem of community detection on networks. The algorithms are based on optimizing the likelihood function of the community assignments given a graph and an estimate of the statistical model that generated it. The optimization framework is inspired by prior work on the unsupervised community detection problem in Stochastic Block Models (SBM) using Semi-Definite Programming (SDP). In this paper we provide the next steps in the evolution of learning communities in this context which involves a constrained semi-definite programming algorithm, and a newly presented active learning algorithm. The active learner intelligently queries nodes that are expected to maximize the change in the model likelihood. Experimental results show that this active learning algorithm outperforms the random-selection semi-supervised version of the same algorithm as well as other state-of-the-art active learning algorithms. Our algorithms significantly improved performance is demonstrated on both real-world and SBM-generated networks even when the SBM has a signal to noise ratio (SNR) below the known unsupervised detectability threshold.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/12/2020

SMACD: Semi-supervised Multi-Aspect Community Detection

Community detection in real-world graphs has been shown to benefit from ...
10/14/2021

Model-Change Active Learning in Graph-Based Semi-Supervised Learning

Active learning in semi-supervised classification involves introducing a...
05/06/2021

Semidefinite Programming for Community Detection with Side Information

This paper produces an efficient Semidefinite Programming (SDP) solution...
01/26/2021

Community Detection in the Stochastic Block Model by Mixed Integer Programming

The Degree-Corrected Stochastic Block Model (DCSBM) is a popular model t...
06/29/2015

Efficient and Parsimonious Agnostic Active Learning

We develop a new active learning algorithm for the streaming setting sat...
06/17/2021

Gone Fishing: Neural Active Learning with Fisher Embeddings

There is an increasing need for effective active learning algorithms tha...
04/13/2014

Active Learning for Undirected Graphical Model Selection

This paper studies graphical model selection, i.e., the problem of estim...