GNNs fail on heterophilous graphs because they assume similar nodes connect; LCC fixes this by explicitly modeling how class labels relate at multiple hops, making it useful for real-world graphs where this assumption breaks down.
This paper tackles a key limitation of graph neural networks: they struggle when nodes with different labels are connected (heterophilous graphs). The authors propose Label Context Classifier (LCC), which captures higher-order patterns in how class labels connect in graphs by using multiple types of walks. LCC can be combined with any GNN to improve node classification performance.