SynHIN: Generating Synthetic Heterogeneous Information Network for
  Explainable AI
Ming-Yi Hong Yi-Hsiang Huang You-Chen Teng Chih-Yu Wang Che Lin
Graph Neural Networks (GNNs) excel in various domains, from detecting
e-commerce spam to social network classification problems. However, the lack of
public graph datasets hampers research progress, particularly in heterogeneous
information networks (HIN). The demand for datasets for fair HIN comparisons is
growing due to advancements in GNN interpretation models. In response, we
propose SynHIN, a unique method for generating synthetic heterogeneous
information networks. SynHIN identifies motifs in real-world datasets,
summarizes graph statistics, and constructs a synthetic network. Our approach
utilizes In-Cluster and Out-Cluster Merge modules to build the synthetic HIN
from primary motif clusters. After In/Our-Cluster mergers and a post-pruning
process fitting the real dataset constraints, we ensure the synthetic graph
statistics align closely with the reference one. SynHIN generates a synthetic
heterogeneous graph dataset for node classification tasks, using the primary
motif as the explanation ground truth. It can adapt and address the lack of
heterogeneous graph datasets and motif ground truths, proving beneficial for
assessing heterogeneous graph neural network explainers. We further present a
benchmark dataset for future heterogeneous graph explainer model research. Our
work marks a significant step towards explainable AI in HGNNs.