A Mixed-Membership Model for Social Network Clustering

TL;DR

This paper introduces a mixed membership model for social network clustering that allows entities to have multiple memberships, using a flexible affinity function and MCMC inference, validated on real and synthetic data.

Contribution

The paper presents a novel mixed membership model with an MCMC algorithm for social network clustering, enabling multiple memberships per entity and improved inference accuracy.

Findings

Effective clustering on Zachary club data

Successful application to dolphin network data

Validated with synthetic network studies

Abstract

We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies based on synthetic networks for further assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly.