The knowledge graph (KG) that represents structural relations among entities has become
an increasingly important research field for knowledge-driven artificial intelligence. In this …

We introduce a transformer-based GNN model, named UGformer, to learn graph
representations. In particular, we present two UGformer variants, wherein the first variant …

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 …

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 However, the issue of outdated facts poses a challenge to KGs, affecting …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 …

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 …