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Knowledge distillation on graphs: A survey
Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …
capability to handle graph data. However, they are difficult to be deployed in resource …
Demystifying structural disparity in graph neural networks: Can one size fit all?
Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …
theoretical evidence supporting their effectiveness in capturing structural patterns on both …
Trustworthy graph neural networks: Aspects, methods and trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …
methods for diverse real-world scenarios, ranging from daily applications like …
Knowledge distillation improves graph structure augmentation for graph neural networks
Graph (structure) augmentation aims to perturb the graph structure through heuristic or
probabilistic rules, enabling the nodes to capture richer contextual information and thus …
probabilistic rules, enabling the nodes to capture richer contextual information and thus …
Towards graph foundation models: A survey and beyond
Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …
foundation models have achieved notable success across natural language processing and …
Graph attention multi-layer perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …
applications. However, the enormous size and high sparsity level of graphs hinder their …
Quantifying the knowledge in gnns for reliable distillation into mlps
To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-
efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well …
efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well …
Symbolic knowledge extraction and injection with sub-symbolic predictors: A systematic literature review
In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by
promoting two complementary activities—symbolic knowledge extraction (SKE) and …
promoting two complementary activities—symbolic knowledge extraction (SKE) and …
Linkless link prediction via relational distillation
Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …
link prediction. Despite their effectiveness, the high latency brought by non-trivial …
Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency
While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-
Euclidean structural data, they are difficult to be deployed in real applications due to the …
Euclidean structural data, they are difficult to be deployed in real applications due to the …