  With the explosive growth of new graduates with research degrees every year,
unprecedented challenges arise for early-career researchers to find a job at a
suitable institution. This study aims to understand the behavior of academic
job transition and hence recommend suitable institutions for PhD graduates.
Specifically, we design a deep learning model to predict the career move of
early-career researchers and provide suggestions. The design is built on top of
scholarly/academic networks, which contains abundant information about
scientific collaboration among scholars and institutions. We construct a
heterogeneous scholarly network to facilitate the exploring of the behavior of
career moves and the recommendation of institutions for scholars. We devise an
unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax)
which aggregates attention mechanism and mutual information for institution
recommendation. Moreover, we propose scholar attention and meta-path attention
to discover the hidden relationships between several meta-paths. With these
mechanisms, HAI provides ordered recommendations with explainability. We
evaluate HAI upon a real-world dataset against baseline methods. Experimental
results verify the effectiveness and efficiency of our approach.
