Hybrid graph attention learning with pseudo-label guided adaptive evolution

Graph Attention Network (GAT), which adaptively distinguishes the importance of neighboring nodes in information aggregation, is a powerful graph representation learning method. However, the existing GAT methods that rely solely on the node embedding-level attention ignore the rich semantic correlation information embedded in the graph topology, struggling to effectively distinguish the importance of different nodes. They also face the challenges as well as semantic deviation between graph and label prediction and insufficient interaction between unlabeled and labeled nodes due to the network depth and scarcity of supervised information. In this paper, we propose a hybrid graph attention learning mechanism to integrate both node embedding-level and structure embedding-level attentions, enabling more comprehensive and accurate modeling of node neighboring relationships. Additionally, we introduce a dynamic graph evolution mechanism that incorporates elaborate topology pruning and node mixing operations guided by pseudo labels with gradually increasing confidence. This endows the model with the ability to adaptively correct the graph structure and significantly enhance its robustness to noisy graph. It is also beneficial for promoting the semantic alignment between graph and label prediction as well as improving the accessibility of labeled nodes. The adaptive graph with feature and structure mixing inevitably promotes the hybrid attention learning, resulting in a closed-loop between representation learning and graph optimization framework. Extensive experimental results on real-world graph datasets clearly demonstrate the superiority of the proposed method in the ability of exploring accurate attention and discriminative representation learning, which achieves significant performance improvements over several previous baselines.