Abstract: Node-level graph contrastive learning(GCL) aims to learn discriminative node representations by constructing high-quality positive and negative sample pairs.However,existing methods suffer from semantic distortion,cross-hierarchy semantic fragmentation,and limited dynamic adaptability due to homogeneous data augmentation and randomly sampled negative pairs.To address these problems,this paper proposes a node level-augmentation free adversarial negative sampling(NL-AFANS) framework.Firstly,an adaptive Gaussian noise injection mechanism is designed based on node degree centrality to dynamically regulate noise intensity for critical nodes,effectively mitigating semantic drift caused by structural perturbations.Secondly,a graph convolutional network(GCN)-based adversarial generator with dual-constraint losses synthesizes topologically valid and semantically challenging high-order hard negative samples,alleviating false-negative contamination and feature redundancy.Concurrently,a hierarchical contrastive architecture jointly optimizes node-level local feature alignment and node-graph-level global mutual information maximization,enhancing cross-hierarchy semantic consistency.Furthermore,a dynamic curriculum strategy with gradient-decoupled alternating training and exponentially increasing difficulty weights for negative samples is introduced to adaptively align progressive learning needs.Experimental results show that NL-AFANS significantly outperforms baseline models such as DGI(Cora:82.3%) and GRACE(Cora:81.7%) in terms of accuracy in unsupervised node classification tasks on mainstream graph datasets like Cora(83.9%) and Photo(93.08%).This work provides new insights into dynamic sample optimization and hierarchical semantic alignment for node-level graph representation learning.

Key words: Graph contrastive learning, Node representation, Dynamic curriculum learning, Adversarial generation, Hierarchical contrast