LIP decomposes GNN message passing to quantify label influences, builds a label influence graph, and propagates high-order effects to outperform prior methods on multi-label node classification benchmarks.
A survey of imbalanced learning on graphs: Problems, techniques, and future directions.arXiv preprint arXiv:2308.13821,
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Multi-Label Node Classification with Label Influence Propagation
LIP decomposes GNN message passing to quantify label influences, builds a label influence graph, and propagates high-order effects to outperform prior methods on multi-label node classification benchmarks.