Knowledge graph (KG) refinement refers to the process of filling in missing information,
removing redundancies, and resolving inconsistencies in KGs. With the growing popularity …

Heterogeneous graph convolutional networks have gained great popularity in tackling
various network analytical tasks on heterogeneous network data, ranging from link …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Heterogeneous graph neural networks have gained great popularity in tackling various
network analysis tasks on heterogeneous network data. However, most existing works …

Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for
link prediction (LP). While numerous studies aim to improve the overall LP performance of …

Background COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of
COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires …

Tracking a targeted subset of nodes in an evolving graph is important for many real-world
applications. Existing methods typically focus on identifying anomalous edges or finding …

Graph clustering discovers groups or communities within networks. Deep learning methods
such as autoencoders (AE) extract effective clustering and downstream representations but …

While open-source communities keep growing at an impressive pace, the corresponding
platforms where software engineers share their work, deliberate about software-related …

In recent years, heterogeneous network representation learning has attracted considerable
attentions with the consideration of multiple node types. However, most of them ignore the …