Extracting low-/high-frequency knowledge from graph neural networks and injecting it into mlps: An effective gnn-to-mlp distillation framework
Recent years have witnessed the great success of Graph Neural Networks (GNNs) in
handling graph-related tasks. However, MLPs remain the primary workhorse for practical …
handling graph-related tasks. However, MLPs remain the primary workhorse for practical …
Graph-based knowledge distillation: A survey and experimental evaluation
Graph, such as citation networks, social networks, and transportation networks, are
prevalent in the real world. Graph Neural Networks (GNNs) have gained widespread …
prevalent in the real world. Graph Neural Networks (GNNs) have gained widespread …
Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification
Graph neural networks (GNNs) can be effectively applied to solve many real‐world
problems across widely diverse fields. Their success is inseparable from the message …
problems across widely diverse fields. Their success is inseparable from the message …
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
Recent years have witnessed great success in handling graph-related tasks with graph
neural networks (GNNs). However, most existing GNNs are based on message passing to …
neural networks (GNNs). However, most existing GNNs are based on message passing to …
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
Recent years have witnessed the great success of graph pre-training for graph
representation learning. With hundreds of graph pre-training tasks proposed, integrating …
representation learning. With hundreds of graph pre-training tasks proposed, integrating …
Clarify confused nodes via separated learning
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented
tasks. However, real-world graphs invariably contain a certain proportion of heterophilous …
tasks. However, real-world graphs invariably contain a certain proportion of heterophilous …
Teaching yourself: Graph self-distillation on neighborhood for node classification
Recent years have witnessed great success in handling graph-related tasks with Graph
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …
Automated graph self-supervised learning via multi-teacher knowledge distillation
Self-supervised learning on graphs has recently achieved remarkable success in graph
representation learning. With hundreds of self-supervised pretext tasks proposed over the …
representation learning. With hundreds of self-supervised pretext tasks proposed over the …
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
Recent years have witnessed great success in handling graph-related tasks with Graph
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …
MS-GDA: Improving Heterogeneous Recipe Representation via Multinomial Sampling Graph Data Augmentation
We study the problem of classifying different cooking styles, based on the recipe. The
difficulty is that the same food ingredients, seasoning, and the very similar instructions result …
difficulty is that the same food ingredients, seasoning, and the very similar instructions result …