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Tracking scientific knowledge flow is challenging. This study introduces a network analysis framework to map interdisciplinary integration and identify knowledge gaps in fields like explainable AI (XAI), aiding innovation.

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Area of Science:

  • Bibliometrics and network science applied to scientific knowledge evolution.

Background:

  • Advancing science requires interdisciplinary knowledge exchange, yet tracking this in evolving fields is difficult.
  • Existing methods struggle to capture the dynamics of knowledge flows and integration in multidisciplinary research areas.

Purpose of the Study:

  • To develop and demonstrate a novel network analysis framework for studying knowledge transfer dynamics.
  • To identify research communities, their life-cycles, and knowledge transfer patterns using citation data.
  • To analyze interdisciplinary integration within the emerging field of explainable Artificial Intelligence (XAI).

Main Methods:

  • Utilizing dynamic community detection on cumulative, time-evolving citation networks.
  • Identifying research areas as groups of papers with shared knowledge sources and outputs.
  • Applying the framework to a case study of eXplainable Artificial Intelligence (XAI) research.

Main Results:

  • Limited knowledge transfer observed between foundational topics (machine learning, statistics, psychology) and contemporary XAI research.
  • Identification of isolated "knowledge silos" within certain application domains of XAI.
  • Significant "knowledge gaps" detected between related XAI research areas, indicating potential for cross-pollination.

Conclusions:

  • The proposed framework effectively maps interdisciplinary integration and identifies knowledge gaps in evolving scientific fields.
  • Findings highlight opportunities for improved knowledge synthesis and innovation in XAI and other multidisciplinary areas.
  • The framework offers valuable insights for literature review, research planning, and science policy by analyzing knowledge ecosystems from citation data.