Network dynamics of innovation processes

TL;DR

This paper presents a network-based model where ideas evolve through reinforced random walks, capturing the emergence and correlation of innovations in scientific knowledge growth.

Contribution

It introduces a novel edge-reinforced random walk model that explains innovation dynamics and reproduces empirical patterns of novelty emergence.

Findings

Model reproduces empirical innovation rates

Captures correlations among innovations

Applicable to real scientific data

Abstract

We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the adjacent possible, and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.