  With the frequent happening of privacy leakage and the enactment of privacy
laws across different countries, data owners are reluctant to directly share
their raw data and labels with any other party. In reality, a lot of these raw
data are stored in the graph database, especially for finance. For
collaboratively building graph neural networks(GNNs), federated learning(FL)
may not be an ideal choice for the vertically partitioned setting where privacy
and efficiency are the main concerns. Moreover, almost all the existing
federated GNNs are mainly designed for homogeneous graphs, which simplify
various types of relations as the same type, thus largely limits their
performance. We bridge this gap by proposing a split learning-based
GNN(SplitGNN), where this model is divided into two sub-models: the local GNN
model includes all the private data related computation to generate local node
embeddings, whereas the global model calculates global embeddings by
aggregating all the participants' local embeddings. Our SplitGNN allows the
isolated heterogeneous neighborhood to be collaboratively utilized. To better
capture representations, we propose a novel Heterogeneous Attention(HAT)
algorithm and use both node-based and path-based attention mechanisms to learn
various types of nodes and edges with multi-hop relation features. We
demonstrate the effectiveness of our SplitGNN on node classification tasks for
two standard public datasets and the real-world dataset. Extensive experimental
results validate that our proposed SplitGNN significantly outperforms the
state-of-the-art(SOTA) methods.
