XSimGCL: Towards extremely simple graph contrastive learning for recommendation
Contrastive learning (CL) has recently been demonstrated critical in improving
recommendation performance. The underlying principle of CL-based recommendation …
recommendation performance. The underlying principle of CL-based recommendation …
Simple and asymmetric graph contrastive learning without augmentations
Abstract Graph Contrastive Learning (GCL) has shown superior performance in
representation learning in graph-structured data. Despite their success, most existing GCL …
representation learning in graph-structured data. Despite their success, most existing GCL …
Graph contrastive learning with stable and scalable spectral encoding
Graph contrastive learning (GCL) aims to learn representations by capturing the agreements
between different graph views. Traditional GCL methods generate views in the spatial …
between different graph views. Traditional GCL methods generate views in the spatial …
A survey on spectral graph neural networks
Graph neural networks (GNNs) have attracted considerable attention from the research
community. It is well established that GNNs are usually roughly divided into spatial and …
community. It is well established that GNNs are usually roughly divided into spatial and …
Architecture matters: Uncovering implicit mechanisms in graph contrastive learning
With the prosperity of contrastive learning for visual representation learning (VCL), it is also
adapted to the graph domain and yields promising performance. However, through a …
adapted to the graph domain and yields promising performance. However, through a …
Spegcl: Self-supervised graph spectrum contrastive learning without positive samples
Graph Contrastive Learning (GCL) excels at managing noise and fluctuations in input data,
making it popular in various fields (eg, social networks, and knowledge graphs). Our study …
making it popular in various fields (eg, social networks, and knowledge graphs). Our study …
Data-centric graph learning: A survey
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
Data-centric graph learning: A survey
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
GALOPA: graph transport learning with optimal plan alignment
Self-supervised learning on graph aims to learn graph representations in an unsupervised
manner. While graph contrastive learning (GCL-relying on graph augmentation for creating …
manner. While graph contrastive learning (GCL-relying on graph augmentation for creating …
PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters
Recently, Graph Contrastive Learning (GCL) has achieved significantly superior
performance in self-supervised graph representation learning. However, the existing GCL …
performance in self-supervised graph representation learning. However, the existing GCL …