Online Estimation and Community Detection of Network Point Processes for Event Streams

TL;DR

This paper introduces a fast online variational inference method for dynamic network community detection using point processes, enabling scalable and real-time analysis of event streams in large networks.

Contribution

It presents a novel online inference algorithm for latent community detection in dynamic networks modeled by point processes, improving scalability and real-time updating capabilities.

Findings

Online inference achieves comparable community recovery to non-online methods.

The proposed method offers significant computational efficiency gains.

The framework is adaptable to various network structures.

Abstract

A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynamic temporal component. A natural way to take account of these temporal dynamics of interactions is to use point processes as the foundation of network models for community detection. Computational complexity hampers the scalability of such approaches to large sparse networks. To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models. We describe this procedure for networks models capturing community structure. This structure can be learned as new events are observed on the network, updating the inferred community assignments. We investigate the theoretical properties of such an inference scheme, and provide regret bounds on the loss function of this procedure. The proposed inference procedure is then thoroughly compared, using both simulation studies and real data, to non-online variants. We demonstrate that online inference can obtain comparable performance, in terms of community recovery, to non-online variants, while realising computational gains. Our proposed inference framework can also be readily modified to incorporate other popular network structures.