SimHawNet: A Modified Hawkes Process for Temporal Network Simulation

TL;DR

SimHawNet is a novel framework that models and simulates the evolution of continuous-time temporal networks using a Hawkes process-based approach, incorporating history-dependent features for accurate and efficient network generation.

Contribution

It introduces a new generative model for continuous-time temporal networks based on Hawkes processes, with a thinning algorithm for simulation and a comprehensive evaluation framework.

Findings

Successfully simulates diverse temporal network evolutions

Achieves comparable performance to state-of-the-art methods

Runs significantly faster than existing approaches

Abstract

Temporal networks allow representing connections between objects while incorporating the temporal dimension. While static network models can capture unchanging topological regularities, they often fail to model the effects associated with the causal generative process of the network that occurs in time. Hence, exploiting the temporal aspect of networks has been the focus of many recent studies. In this context, we propose a new framework for generative models of continuous-time temporal networks. We assume that the activation of the edges in a temporal network is driven by a specified temporal point process. This approach allows to directly model the waiting time between events while incorporating time-varying history-based features as covariates in the predictions. Coupled with a thinning algorithm designed for the simulation of point processes, SimHawNet enables simulation of the evolution of temporal networks in continuous time. Finally, we introduce a comprehensive evaluation framework to assess the performance of such an approach, in which we demonstrate that SimHawNet successfully simulates the evolution of networks with very different generative processes and achieves performance comparable to the state of the art, while being significantly faster.