Graph context-attention network via low and high order aggregation

Abstract

Graph attention networks (GATs) have been shown effectively for representation learning. However, existing GATs only employ the first-order attention mechanism and thus fail to fully exploit and learn node’s contextual feature representations. To address this issue, we propose a novel Graph Context-Attention Network (GCAN) via low and high order aggregation for representation learning. The proposed model fully exploits both low-order and high-order information of nodes for representation learning by employing high-order attention mechanism and adversary regularization constraints. The main benefit of the proposed method is that it can effectively capture the discriminative feature differences among node representations while enhancing the diversity and richness of node features. Thus it can alleviate the issue of over-smoothing in the network learning. We perform extensive experiments on nine datasets and the experimental results demonstrate the effectiveness and better performance of our approach.