  In recent years, Graph Neural Networks has received enormous attention from
academia for its huge potential of modeling the network traits such as
macrostructure and single node attributes. However, prior mainstream works
mainly focus on homogeneous network and lack the capacity to characterize the
network heterogeneous property. Besides, most previous literature cannot model
the influence under microscope vision, making it infeasible to model the joint
relation between the heterogeneity and mutual interaction within multiple
relation type. In this paper, we propose an Influence Self-attention network to
address the difficulties mentioned above. To model heterogeneity and mutual
interaction, we redesign attention mechanism with influence factor on the
single-type relation level, which learns the importance coefficient from its
adjacent neighbors under the same meta-path based patterns. To incorporate the
heterogeneous meta-path in a unified dimension, we developed a self-attention
based framework for meta-path relation fusion according to the learned
meta-path coefficient. Our experimental results demonstrate that our framework
not only achieve higher results than current state-of-the-art baselines, but
also show promising vision on depicting heterogeneous interactive relations
under complicated network structure.
