Bayesian Nonparametrics for Sparse Dynamic Networks
This work addresses the challenge of analyzing dynamic networks with sparsity for applications like social media and text analysis, representing an incremental advancement in network modeling methods.
The paper tackled the problem of modeling sparse time-varying networks by proposing a Bayesian nonparametric approach that associates sociability parameters to nodes, evolving via a dynamic point process, and demonstrated its ability to capture long-term evolution and yield sparse graphs with subquadratic edge growth.
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.