Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Abstract

Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

Tech mining: a revisit and navigation

“…Examining 177 Web of Science (WoS) abstract records from a search on either “Tech Mining” or “research profiling” – alerts us to some associated keywords: technical intelligence, science mapping, and Semantic TRIZ. From our experience, in recent years, we see increasing Tech Mining engagement of: intelligent bibliometrics (incorporating various AI capabilities) (Zhang et al, 2020 , 2021a ), knowledge modeling (to identify related research, not limited to use of particular terms in searching) (Cassidy, 2020 ; Wu et al, 2023 ), and Literature-Based Discovery (to identify related research falling outside one's topical search domain) (Porter et al, 2020 ; Zhang et al, 2023b ).…”

“…Despite potential challenges in understanding complicated ST&I patterns, we anticipate in-depth engagement between Tech Mining and AI in proposing novel solutions for the How and Why questions re: ST&I development. Our attempt to develop a heterogeneous knowledge graph mining approach to track knowledge trajectories is one example (Zhang et al, 2023b ).…”