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Found 5 papers, 2 papers with code
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks.
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Besides, we observe that learning from multiple philosophies enhances not only the task generalization but also the single task performances, demonstrating that PARETOGNN achieves better task generalization via the disjoint yet complementary knowledge learned from different philosophies.
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
The effectiveness is further guaranteed and proved by the gradients' distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative for providing gradients to update model parameters.
Graph Contrastive Learning with Cross-view Reconstruction
In light of this, we introduce the Graph Contrastive Learning with Cross-View Reconstruction (GraphCV), which follows the information bottleneck principle to learn minimal yet sufficient representation from graph data.
Gophormer: Ego-Graph Transformer for Node Classification
Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.
