In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.
In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks.
Classification problems have made significant progress due to the maturity of artificial intelligence (AI).
Three schemes are considered: a symmetric model, in which all lanes are driving lanes, an asymmetric model, in which the right lane is a driving lane and the other lanes are overtaking lanes, a hybrid model, in which the leftmost lane is an overtaking lane and all the other lanes are driving lanes.
Physics and Society Cellular Automata and Lattice Gases