Node classification in heterogeneous graphs, where nodes and/or edges have multiple types.
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With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.
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.
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
#7 best model for Heterogeneous Node Classification on DBLP (PACT) 14k
In this way, it leverages both local and non-local information simultaneously.
SOTA for Heterogeneous Node Classification on DBLP (PACT) 14k (Macro-F1 (60% training data) metric )