We introduce a transformer-based GNN model, named UGformer, to learn graph representations. In particular, we present two UGformer variants, wherein the first variant …
C He, J Cheng, X Fei, Y Weng, Y Zheng… - Knowledge-Based Systems, 2023 - Elsevier
Link prediction in attributed networks has attracted increasing attention recently due to its valuable real-world applications. Various related methods have been proposed, but most of …
Federated learning (FL) can be essential in knowledge representation, reasoning, and data mining applications over multi-source knowledge graphs (KGs). A recent study FedE first …
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations …
Spatial audio methods are gaining a growing interest due to the spread of immersive audio experiences and applications, such as virtual and augmented reality. For these purposes …
Knowledge graph (KG) alignment and completion are usually treated as two independent tasks. While recent work has leveraged entity and relation alignments from multiple KGs …
H Tu, S Yu, V Saikrishna, F Xia… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting …
In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness works …
W Wei, Y Song, B Yao - … Transactions on Knowledge Discovery from Data, 2024 - dl.acm.org
Knowledge graphs (KGs) play a vital role in enhancing search results and recommendation systems. With the rapid increase in the size of KGs, they are becoming inaccurate and …