A Variational Topological Neural Model for Cascade-based Diffusion in Networks

TL;DR

This paper introduces a novel variational topological neural model that captures the influence of hidden content trajectories on diffusion dynamics in networks, improving modeling and prediction accuracy.

Contribution

It combines graphical Markovian and recurrent neural approaches by embedding diffusion history as hidden states, addressing the challenge of hidden content trajectories.

Findings

Good experimental performance in diffusion modeling

Effective in predicting cascade spread

Handles hidden content trajectories successfully

Abstract

Many works have been proposed in the literature to capture the dynamics of diffusion in networks. While some of them define graphical markovian models to extract temporal relationships between node infections in networks, others consider diffusion episodes as sequences of infections via recurrent neural models. In this paper we propose a model at the crossroads of these two extremes, which embeds the history of diffusion in infected nodes as hidden continuous states. Depending on the trajectory followed by the content before reaching a given node, the distribution of influence probabilities may vary. However, content trajectories are usually hidden in the data, which induces challenging learning problems. We propose a topological recurrent neural model which exhibits good experimental performances for diffusion modelling and prediction.