Structural Influence and Label Conflict Aware Based Graph Curriculum Learning Approach

In recent years,graph neural networks(GNNs) have emerged as a prominent research area in the field of graph lear-ning.Leveraging the message passing mechanism,GNNs have showcased remarkable performance across diverse graph-based tasks.However,most existing GNNs methods assume uniform training difficulty across all nodes,disregarding the significant va-riability in the importance and contributions of different nodes.To address this problem,this paper proposes a structural influence and label conflict aware graph curriculum learning method(SILC-GCL),which takes the training difficulty of nodes into conside-ration.To begin with,a difficulty measure is designed through both the graph structure and node label semantics,calculating the PageRank value of nodes and the label conflict degree between nodes and their neighbors.Subsequently,a training scheduler is employed to select nodes with appropriate training difficulty at each training stage and then generate a sequence of training nodes from easy to difficult.Finally,SILC-GCL is trained based on backbone GNNs models.Experimental results of node classification on six benchmark datasets verify the effectiveness of SILC-GCL.