Activating the "Breakfast Club": Modeling Influence Spread in Natural-World Social Networks

TL;DR

This paper introduces the Activation Jump Model (AJM), a new influence diffusion model for physical social networks that accounts for multi-agent effects and non-neighbor influence, outperforming traditional models in real-world data.

Contribution

The paper proposes the AJM, a novel influence spread model that captures multi-agent team effects and non-local influence in physical networks, improving fit to empirical data.

Findings

AJM outperforms existing models in fitting real-world influence data.

AJM exhibits well-behaved properties similar to dominant influence models.

The model offers a more flexible and accurate approach for influence maximization in physical networks.

Abstract

While reigning models of diffusion have privileged the structure of a given social network as the key to informational exchange, real human interactions do not appear to take place on a single graph of connections. Using data collected from a pilot study of the spread of HIV awareness in social networks of homeless youth, we show that health information did not diffuse in the field according to the processes outlined by dominant models. Since physical network diffusion scenarios often diverge from their more well-studied counterparts on digital networks, we propose an alternative Activation Jump Model (AJM) that describes information diffusion on physical networks from a multi-agent team perspective. Our model exhibits two main differentiating features from leading cascade and threshold models of influence spread: 1) The structural composition of a seed set team impacts each individual node's influencing behavior, and 2) an influencing node may spread information to non-neighbors. We show that the AJM significantly outperforms existing models in its fit to the observed node-level influence data on the youth networks. We then prove theoretical results, showing that the AJM exhibits many well-behaved properties shared by dominant models. Our results suggest that the AJM presents a flexible and more accurate model of network diffusion that may better inform influence maximization in the field.