Graph contrastive learning with generative adversarial network
Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node
representations for many downstream tasks through supervised end-to-end training. To deal …
representations for many downstream tasks through supervised end-to-end training. To deal …
A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges
Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks.
However, most existing methods have the homogeneity assumption and show poor …
However, most existing methods have the homogeneity assumption and show poor …
Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework
C Wu, S Shi, C Wang, Z Liu, W Peng, W Wu… - Proceedings of the …, 2024 - dl.acm.org
Recommender systems have emerged as an indispensable mean to meet personalized
interests of users and alleviate information overload. Despite the great success, accuracy …
interests of users and alleviate information overload. Despite the great success, accuracy …
[PDF][PDF] GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs
Graph representation learning is an emerging task for effectively embedding graph-
structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods …
structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods …