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SCALABLE MCMC IN DEGREE CORRECTED STOCHASTIC BLOCK MODEL

Citation Author(s):
Soumyasundar Pal, Mark Coates
Submitted by:
SOUMYASUNDAR PAL
Last updated:
10 May 2019 - 1:51pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Soumyasundar Pal
Paper Code:
3720
 

Community detection from graphs has many applications
in machine learning, biological and social sciences. While
there is a broad spectrum of literature based on various
approaches, recently there has been a significant focus on
inference algorithms for statistical models of community
structure. These algorithms strive to solve an inference
problem based on a generative model of the network. Recent
advances in stochastic gradient MCMC have played a crucial
role in improving the scalability of these techniques. In this
paper, we propose a version of a degree corrected stochastic
block model and present an MCMC based inference algorithm.
Experimental results on several real world networks
demonstrate the effectiveness of the proposed approach.

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