SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention

• URL: http://arxiv.org/abs/2301.12885v1
• Date: Fri, 27 Jan 2023 12:08:44 GMT
• Title: SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
• Authors: Xiaolong Xu and Lingjuan Lyu and Yihong Dong and Yicheng Lu and Weiqiang Wang and Hong Jin
• Abstract summary: We propose a split learning-based graph neural network (SplitGNN) for graph computation. Our SplitGNN allows the isolated heterogeneous neighborhood to be collaboratively utilized. We demonstrate the effectiveness of our SplitGNN on node classification tasks for two standard public datasets and the real-world dataset.
• Score: 29.307331758493323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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