Improving Article Classification with Edge-Heterogeneous Graph Neural
  Networks
Khang Ly Yury Kashnitsky Savvas Chamezopoulos Valeria Krzhizhanovskaya
Classifying research output into context-specific label taxonomies is a
challenging and relevant downstream task, given the volume of existing and
newly published articles. We propose a method to enhance the performance of
article classification by enriching simple Graph Neural Networks (GNN)
pipelines with edge-heterogeneous graph representations. SciBERT is used for
node feature generation to capture higher-order semantics within the articles'
textual metadata. Fully supervised transductive node classification experiments
are conducted on the Open Graph Benchmark (OGB) ogbn-arxiv dataset and the
PubMed diabetes dataset, augmented with additional metadata from Microsoft
Academic Graph (MAG) and PubMed Central, respectively. The results demonstrate
that edge-heterogeneous graphs consistently improve the performance of all GNN
models compared to the edge-homogeneous graphs. The transformed data enable
simple and shallow GNN pipelines to achieve results on par with more complex
architectures. On ogbn-arxiv, we achieve a top-15 result in the OGB competition
with a 2-layer GCN (accuracy 74.61%), being the highest-scoring solution with
sub-1 million parameters. On PubMed, we closely trail SOTA GNN architectures
using a 2-layer GraphSAGE by including additional co-authorship edges in the
graph (accuracy 89.88%). The implementation is available at:
$\href{https://github.com/lyvykhang/edgehetero-nodeproppred}{\text{https://github.com/lyvykhang/edgehetero-nodeproppred}}$.