Knowledge Transfer, Knowledge Gaps, and Knowledge Silos in Citation Networks

TL;DR

The paper addresses how knowledge flows across disciplines in rapidly evolving fields, where static taxonomies fail to capture dynamic interdisciplinary interactions. It introduces a dynamic citation-network framework that identifies research areas as step and dynamic communities using OSLOM on cumulative graphs $G_t$, labels them with TF-ICF/CtD, and quantifies knowledge transfer via a community interaction network with edge probabilities $p_{ij}^t$, complemented by SciBERT-based content coherence and second-order proximity analyses. In a case study of XAI, foundational topics such as CS, statistics, and psychology are shown to be important knowledge sources, but knowledge transfer to contemporary topics is uneven, with notable knowledge silos (e.g., environmental science applications and COVID-19 imaging) and several knowledge gaps detected via residual analysis in a gamma regression framework. The framework enables data-driven mapping of knowledge ecosystems, informing literature reviews, research planning, and science policy by revealing how interdisciplinary integration unfolds over time and where cross-pollination is most needed.

Abstract

The advancement of science relies on the exchange of ideas across disciplines and the integration of diverse knowledge domains. However, tracking knowledge flows and interdisciplinary integration in rapidly evolving, multidisciplinary fields remains a significant challenge. This work introduces a novel network analysis framework to study the dynamics of knowledge transfer directly from citation data. By applying dynamic community detection to cumulative, time-evolving citation networks, we can identify research areas as groups of papers sharing knowledge sources and outputs. Our analysis characterises the life-cycles and knowledge transfer patterns of these dynamic communities over time. We demonstrate our approach through a case study of eXplainable Artificial Intelligence (XAI) research, an emerging interdisciplinary field at the intersection of machine learning, statistics, and psychology. Key findings include: (i) knowledge transfer between these important foundational topics and the contemporary topics in XAI research is limited, and the extent of knowledge transfer varies across different contemporary research topics; (ii) certain application domains exist as isolated "knowledge silos"; (iii) significant "knowledge gaps" are identified between related XAI research areas, suggesting opportunities for cross-pollination and improved knowledge integration. By mapping interdisciplinary integration and bridging knowledge gaps, this work can inform strategies to synthesise ideas from disparate sources and drive innovation. More broadly, our proposed framework enables new insights into the evolution of knowledge ecosystems directly from citation data, with applications spanning literature review, research planning, and science policy.

Figures (5)

• Figure 1: Flow diagram showing the life-cycle of a dynamic community pertaining to statistics research on regression models. Any papers present in the first or last realisation of the dynamic community are plotted. The nodes in the graph represent step communities and they are grouped by the time step in which they appear. The edges between nodes show the movement of papers between step communities. The dynamic community dissolves after 2010
• Figure 2: Flow diagram showing the life-cycle of a dynamic community of neural networks research that splits before 2005. The research area that focuses on rule extraction remains present in 2020. Any papers present in the first or last realisation of the dynamic community are plotted. The nodes in the graph represent step communities and they are grouped by the time step in which they appear. The edges between nodes show the movement of papers between step communities.
• Figure 3: Dynamic community life-cycles of the communities with the highest betweenness centrality (left) and lowest betweenness centrality (right) in the period 2000--2010. Each row represents the life-cycle of a dynamic community and each cell in the row is populated if that dynamic community appears in the network as a step community in the corresponding time step. Each cell is coloured to show the most common ASJC category among the papers in the step community.
• Figure 4: The percentage of total possible interactions (citations) between contemporary research ares in XAI literature and the foundation areas. For comparison, some recent central topics in XAI are included on the right. For readability, the interaction probabilities are scaled to percentages. For example, a score of 2% between two research areas indicates that 20% of papers in research area A cite 10% of papers in research area B.
• Figure 5: The percentage of total possible interactions (citations) between contemporary research areas in XAI literature and the foundation areas. For comparison, some recent central topics in XAI are included on the right. For readability, the interaction probabilities are scaled to percentages. For example, if 20% of papers in research area A cite 10% of papers in research area B, the resulting interactions score would be 2%.