Bayesian Nonparametrics for Sparse Dynamic Networks

TL;DR

This paper introduces a Bayesian nonparametric model for sparse, dynamic networks that captures long-term sociability evolution and produces subquadratic growth in edges, demonstrated on various datasets.

Contribution

It presents a novel dynamic point process model using a generalized gamma process for modeling evolving sociabilities in sparse networks.

Findings

Model captures long-term sociability evolution.

Produces sparse graphs with subquadratic edge growth.

Successfully applied to real and simulated datasets.

Abstract

In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is able to capture long term evolution of the sociabilities. Moreover, it yields sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying generalised gamma process. We provide some theoretical insights into the model and apply it to three datasets: a simulated network, a network of hyperlinks between communities on Reddit, and a network of co-occurences of words in Reuters news articles after the September 11th attacks.