SHGNN: Structure-Aware Heterogeneous Graph Neural Network
- URL: http://arxiv.org/abs/2112.06244v2
- Date: Tue, 14 Dec 2021 07:37:14 GMT
- Title: SHGNN: Structure-Aware Heterogeneous Graph Neural Network
- Authors: Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu
- Abstract summary: This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations.
We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path.
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
- Score: 77.78459918119536
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Many real-world graphs (networks) are heterogeneous with different types of
nodes and edges. Heterogeneous graph embedding, aiming at learning the
low-dimensional node representations of a heterogeneous graph, is vital for
various downstream applications. Many meta-path based embedding methods have
been proposed to learn the semantic information of heterogeneous graphs in
recent years. However, most of the existing techniques overlook the graph
structure information when learning the heterogeneous graph embeddings. This
paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network
(SHGNN) to address the above limitations. In detail, we first utilize a feature
propagation module to capture the local structure information of intermediate
nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate
the graph structure information into the aggregation module on the meta-path.
Finally, we leverage a meta-path aggregator to fuse the information aggregated
from different meta-paths. We conducted experiments on node classification and
clustering tasks and achieved state-of-the-art results on the benchmark
datasets, which shows the effectiveness of our proposed method.
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