Abstract Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot
make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open …

Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various
tasks, but we cannot accurately know the importance of different design dimensions of …

Negative sampling (NS) loss plays an important role in learning knowledge graph
embedding (KGE) to handle a huge number of entities. However, the performance of KGE …

Graph neural networks (GNNs), which effectively use topological structures in the
knowledge graphs (KG) to embed entities and relations in low-dimensional spaces, have …

As hyper-parameters are ubiquitous and can significantly affect the model performance,
hyper-parameter optimization is extremely important in machine learning. In this paper, we …

Knowledge graph (KG) embedding is a fundamental task in natural language processing,
and various methods have been proposed to explore semantic patterns in distinctive ways …

Automated Machine Learning (AutoML) techniques have recently been introduced to design
Collaborative Filtering (CF) models in a data-specific manner. However, existing works …

Relation prediction on knowledge graphs (KGs) attempts to infer the missing links between
entities. Most previous studies are limited to the transductive setting where all entities must …

Graph Neural Networks (GNNs) have been widely used in Collaborative Filtering (CF).
However, when given a new recommendation scenario, the current options are either …

Graph-structured data are the commonly used and have wide application scenarios in the
real world. For these diverse applications, the vast variety of learning tasks, graph domains …