  Interest in tracing the research interests of scientific researchers is
rising, and particularly that of predicting a researcher's knowledge
trajectories beyond their current foci into potential
inter-/cross-/multi-disciplinary interactions. Hence, 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 reflect
real-world academic activity, such as knowledge sharing between co-authors or
diffusing between similar research topics. This strategy differentiates the
interactions occurring between homogeneous and heterogeneous nodes and weights
the strengths of these interactions. Two sets of experiments - one with a local
dataset and another with a global dataset - demonstrate that the proposed
method is prior to selected baselines. In addition, to further examine the
reliability of our method, we conducted a case study on recommending knowledge
trajectories of selected information scientists and their research groups. The
results demonstrate the empirical insights our method yields for individual
researchers, communities, and research institutions in the information science
discipline.
