  Academic networks in the real world can usually be described by heterogeneous
information networks composed of multi-type nodes and relationships. Some
existing research on representation learning for homogeneous information
networks lacks the ability to explore heterogeneous information networks in
heterogeneous information networks. It cannot be applied to heterogeneous
information networks. Aiming at the practical needs of effectively identifying
and discovering scientific research teams from the academic heterogeneous
information network composed of massive and complex scientific and
technological big data, this paper proposes a scientific research team
identification method based on representation learning of academic
heterogeneous information networks. The attention mechanism at node level and
meta-path level learns low-dimensional, dense and real-valued vector
representations on the basis of retaining the rich topological information of
nodes in the network and the semantic information based on meta-paths, and
realizes effective identification and discovery of scientific research teams
and important team members in academic heterogeneous information networks based
on maximizing node influence. Experimental results show that our proposed
method outperforms the comparative methods.
