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Found 5 papers, 4 papers with code
A Survey on Structure-Preserving Graph Transformers
The transformer architecture has shown remarkable success in various domains, such as natural language processing and computer vision.
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
In this paper, we propose Community-aware Graph Transformers, namely CGT, to learn degree-unbiased representations based on learnable augmentations and graph transformers by extracting within community structures.
Ranked #1 on
Node Clustering
on Pubmed
(Conductance metric)
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG.
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations.
Ranked #1 on
Node Clustering
on Actor
Connector 0.5: A unified framework for graph representation learning
Second, we introduce various graph representation learning models, ranging from shallow to deep graph embedding models.
