High-order graph attention network

GCN is a widely-used representation learning method for capturing hidden features in graph data. However, traditional GCNs suffer from the over-smoothing problem, hindering their ability to extract high-order information and obtain robust data representation. To overcome this limitation, we propose a novel graph model, the high-order graph attention network. Compared to other existing graph attention networks, our model can adaptively aggregate node features from multi-hop neighbors through an attention mechanism. Moreover, the edges in the original graph may not accurately represent the relationships between nodes. We implement a new approach to update the graph by using the aggregated node representation to adjust the edges with small step sizes. Additionally, we perform a theoretical analysis to demonstrate the relationships between our proposed model and other GCN models. Finally, we evaluate our proposed model against eight variants of GCN models on multiple widely-used benchmark datasets. The experimental results show the superiority of our proposed model over other models.