Seeking Similarities While Removing Differences: Graph Neural Networks Based on Node Correlation

Graph neural networks (GNNs) have proven highly effective in handling graph-structured data. However, most existing GNNs rely on the homophily assumption, hindering their performance on heterophilic graphs. This limitation is partially due to aggregation containing irrelevant nodes. In this work, we propose a novel GNN model based on node correlation called NoC-GNN, to address the deficiencies of existing techniques. NoC-GNN retains relevant nodes while removing irrelevant nodes, enhancing the effectiveness of nodes in the mixed state. NoC-GNN first constructs a new graph structure based on the k-nearest neighbor (kNN) graph to aggregate relevant nodes, and then constructs a matrix based on the new graph structure to remove possibly irrelevant nodes. Finally, the attention mechanism is used to adaptively integrate relevant, irrelevant, and self-information to model both homophilic and heterophilic graphs. Experimental results demonstrate that NoC-GNN achieves superior performance across a wide range of semi-supervised node classification tasks.