  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.
