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Found 7 papers, 7 papers with code
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods.
Self-Guided Robust Graph Structure Refinement
However, we have discovered that existing GSR methods are limited by narrowassumptions, such as assuming clean node features, moderate structural attacks, and the availability of external clean graphs, resulting in the restricted applicability in real-world scenarios.
Adaptive Self-training Framework for Fine-grained Scene Graph Generation
To this end, we introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained.
LLM4SGG: Large Language Models for Weakly Supervised Scene Graph Generation
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations.
Class Label-aware Graph Anomaly Detection
Unsupervised GAD methods assume the lack of anomaly labels, i. e., whether a node is anomalous or not.
Similarity Preserving Adversarial Graph Contrastive Learning
Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features.
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment
Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i. e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes.
