4 papers
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly r…
Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph
Ziyu Zheng, Yaming Yang, Ziyu Guan +3
The ``pre-train, prompt" paradigm, designed to bridge the gap between pre-training tasks and downstream objectives, has been extended from the NLP domain to the graph domain and ha…
Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree
Yaming Yang, Zhe Wang, Ziyu Guan +3
Entity Alignment (EA) is to link potential equivalent entities across different knowledge graphs (KGs). Most existing EA methods are supervised as they require the supervision of s…
Aligning Multiple Knowledge Graphs in a Single Pass
Yaming Yang, Zhe Wang, Ziyu Guan +5
Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods m…