Datasets play a central role in scholarly communications. However, scholarly graphs are
often incomplete, particularly due to the lack of connections between publications and …

Open access to datasets is increasingly driving modern science. Consequently, discovering
such datasets is becoming an important functionality for scientists in many different fields …

In this paper, we study the graph-based semi-supervised learning for classifying nodes in
attributed networks, where the nodes and edges possess content information. Recent …

Scientific items (such as papers or datasets) discovery and reuse is crucial to support and
improve scientific research. However, the process is often tortuous, and researchers end up …

Network embedding is a process of encoding nodes into latent vectors by preserving
network structure and content information. It is used in various applications, especially in …

In today's information and computational society, complex systems are often modeled as
multi-modal networks associated with heterogeneous structural relation, unstructured …

The increase in e-commerce activity and research has led to growing demand for dataset
recommendation systems that support data-driven decision-making, such as refining pricing …

Scientific datasets are important in scientific research. Researchers always want to find a
way to improve their research. Data discovery and reuse is a recent and popular way of …

Research and experimentation in various scientific fields are based on the knowledge and
ideas from scholarly literature. The advancement of research and development has, thus …

We provide new formulas for the Wiener–Hopf factorization indices of a rational matrix
function R which has neither poles nor zeros on the unit circle. In addition, we recover recent …