5 papers
Common-Neighbor-Count-Based Representative Possible World Finding on Uncertain Graphs
Chengjie Gu, Xiaoliang Xu, Yuxiang Wang +5
A representative possible world (RPW) is a deterministic graph derived from an uncertain graph where a designated structural feature closely approximates its expected…
Multi-Faceted Continual Knowledge Graph Embedding for Semantic-Aware Link Prediction
Jing Qi, Yuxiang Wang, Zhiyuan Yu +3
Continual Knowledge Graph Embedding (CKGE) aims to continually learn embeddings for new knowledge, i.e., entities and relations, while retaining previously acquired knowledge. Most…
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
Yuxia Geng, Runkai Zhu, Jiaoyan Chen +7
Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the featur…
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Yuxia Geng, Jiaoyan Chen, Yuhang Zeng +5
Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they ha…
Graph Similarity Computation via Interpretable Neural Node Alignment
Jingjing Wang, Hongjie Zhu, Haoran Xie +3
\Graph similarity computation is an essential task in many real-world graph-related applications such as retrieving the similar drugs given a query chemical compound or finding the…