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Found 6 papers, 0 papers with code
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs
Then, we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them.
Supervised Contrastive Learning with Structure Inference for Graph Classification
In this paper, we propose a novel graph neural network based on supervised contrastive learning with structure inference for graph classification.
Multi-task Self-distillation for Graph-based Semi-Supervised Learning
In this paper, we propose a multi-task self-distillation framework that injects self-supervised learning and self-distillation into graph convolutional networks to separately address the mismatch problem from the structure side and the label side.
Latent Network Embedding via Adversarial Auto-encoders
To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders.
Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation
Modern graph neural networks provide facilitation to jointly capture the above information in attributed graphs with a feature aggregation manner, and have achieved great success in attributed graph clustering.
Diffusion Based Network Embedding
In order to solve the limitations, we propose in this paper a network diffusion based embedding method.
