Beyond Pairwise Distance: Cognitive Traversal Distance as a Holistic Measure of Scientific Novelty

TL;DR

This paper introduces Cognitive Traversal Distance (CTD), a network-based measure of scientific novelty that considers the minimal structural path connecting all knowledge units of a paper, outperforming traditional pairwise aggregation methods.

Contribution

The paper proposes a novel network-based indicator, CTD, for measuring scientific novelty at the paper level, moving beyond pairwise distance aggregation methods.

Findings

CTD outperforms traditional aggregation-based novelty indicators.

CTD is less sensitive to new conceptual labels compared to text-based measures.

Evaluation on 27 million biomedical publications validates CTD's effectiveness.

Abstract

Scientific novelty is a critical construct in bibliometrics and is commonly measured by aggregating pairwise distances between the knowledge units underlying a paper. While prior work has refined how such distances are computed, less attention has been paid to how dyadic relations are aggregated to characterize novelty at the paper level. We address this limitation by introducing a network-based indicator, Cognitive Traversal Distance (CTD). Conceptualizing the historical literature as a weighted knowledge network, CTD is defined as the length of the shortest path required to connect all knowledge units associated with a paper. CTD provides a paper-level novelty measure that reflects the minimal structural distance needed to integrate multiple knowledge units, moving beyond mean- or quantile-based aggregation of pairwise distances. Using 27 million biomedical publications indexed by OpenAlex and Medical Subject Headings (MeSH) as standardized knowledge units, we evaluate CTD against expert-based novelty benchmarks from F1000Prime-recommended papers and Nobel Prize-winning publications. CTD consistently outperforms conventional aggregation-based indicators. We further show that MeSH-based CTD is less sensitive to novelty driven by the emergence of entirely new conceptual labels, clarifying its scope relative to recent text-based measures.