Self-supervised Smoothing Graph Neural Networks

This paper studies learning node representations with GNNs for unsupervised scenarios. We make a theoretical understanding and empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined... (read more)

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Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Node Classification PubMed with Public Split: fixed 20 nodes per class K2SL-GCN Accuracy 83.8% # 1

Methods used in the Paper


METHOD TYPE
GCN
Graph Models