Instead of directly using distance information and cartesian coordinates, directional information determined by angles and distances of spherical coordinates was used to construct graph edge features. D-CGCNN achieved higher prediction accuracy for the IS2RE task compared to other previous models, and successfully discovered stable materials from unrelaxed dataset generated by the elemental substitution. Furthermore, we demonstrated a correlation between graph similarities and model performances for IS2RE and RS2RE tasks, supporting that the improved prediction accuracy of D-CGCNN for IS2RE task is originated from our unique representation of crystal structures.