Information diffusion backbones in temporal networks

TL;DR

This paper investigates how local and temporal connection features influence the likelihood of node pairs appearing in diffusion paths within temporal networks, emphasizing the importance of temporal information in predicting diffusion roles.

Contribution

It introduces a method to construct diffusion backbones in temporal networks and demonstrates the predictive power of temporal features over traditional integrated network features.

Findings

Temporal features outperform integrated network features in predicting diffusion links.

Node pairs with many early contacts are more likely to appear in diffusion processes.

Different infection probabilities produce related but distinct diffusion backbones.

Abstract

Much effort has been devoted to understand how temporal network features and the choice of the source node affect the prevalence of a diffusion process. In this work, we addressed the further question: node pairs with what kind of local and temporal connection features tend to appear in a diffusion trajectory or path, thus contribute to the actual information diffusion. We consider the Susceptible-Infected spreading process with a given infection probability per contact on a large number of real-world temporal networks. We illustrate how to construct the information diffusion backbone where the weight of each link tells the probability that a node pair appears in a diffusion process starting from a random node. We unravel how these backbones corresponding to different infection probabilities relate to each other and point out the importance of two extreme backbones: the backbone with infection probability one and the integrated network, between which other backbones vary. We find that the temporal node pair feature that we proposed could better predict the links in the extreme backbone with infection probability one as well as the high weight links than the features derived from the integrated network. This universal finding across all the empirical networks highlights that temporal information are crucial in determining a node pair's role in a diffusion process. A node pair with many early contacts tends to appear in a diffusion process. Our findings shed lights on the in-depth understanding and may inspire the control of information spread.