Activity date estimation in timestamped interaction networks

TL;DR

This paper introduces a latent space generative model for timestamped interaction networks that estimates global activity dates in historical data, outperforming local averages in dense networks.

Contribution

The paper presents a novel latent space model specifically designed for estimating activity dates in timestamped interaction networks, improving accuracy over traditional local averaging methods.

Findings

Model outperforms local averages in dense networks

Provides more accurate global activity date estimates

Effective in historical network analysis

Abstract

We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks