Enhancing Graph Contrastive Learning by Capturing Dimensional Rationale from a Causal Perspective
Capturing the dimensional rationale from graphs can improve the discriminability and transferability of graph representations learned by contrastive learning.
Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
A novel collaborative graph contrastive learning framework (CGCL) that generates contrastive views from multiple graph encoders without relying on handcrafted data augmentations.
MPXGAT: An Attention-Based Deep Learning Model for Embedding Multiplex Networks and Predicting Inter-Layer Links
MPXGAT is an attention-based deep learning model that can effectively embed multiplex networks and accurately predict both intra-layer and inter-layer links.
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
그래프 표현 학습을 위한 제어 기반 그래프 임베딩의 효과적인 활용
Exploring Task Unification in Graph Representation Learning via Generative Approach
GA2E proposes a unified adversarially masked autoencoder to seamlessly address challenges in graph representation learning.
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
グラフ表現学習における制御ベースのグラフ埋め込みの重要性と効果を強調します。
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
The author introduces Control-based Graph Contrastive Learning (CGCL) as a novel framework for unsupervised graph representation learning, leveraging graph controllability properties and advanced edge augmentation methods to create augmented data for contrastive learning while preserving the controllability rank of graphs.
Few-Shot Learning on Graphs: Meta-learning, Pre-training, and Hybrid Approaches
The author explores the advancements in few-shot learning on graphs through meta-learning, pre-training, and hybrid approaches to address the challenge of limited labeled data availability. The survey categorizes existing studies into three major families and outlines future research directions.
A Comprehensive Survey on Deep Graph Representation Learning: Techniques, Challenges, and Future Directions
Deep graph representation learning combines the strengths of graph kernels and neural networks to capture complex structural information in graphs while learning abstract representations. This survey explores various graph convolution techniques, challenges, and future research directions.
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