Learning graph representations is a fundamental task aimed at capturing various properties
of graphs in vector space. The most recent methods learn such representations for static …

The prediction of interactions in protein networks is very critical in various biological
processes. In recent years, scientists have focused on computational approaches to predict …

Most real-world networks are heterogeneous and consist of different types of nodes and
edges. Relationships (edges) between nodes of different types carry different semantics …

While thousands of new mobile applications (ie, apps) are being added to the major app
markets daily, only a small portion of them attain their financial goals and survive in these …

Node similarity measures quantify how similar a pair of nodes are in a network. These
similarity measures turn out to be an important fundamental tool for many real world …

Link prediction algorithms aim to infer the existence of connections (or links) between nodes
in network-structured data and are typically applied to refine the connectivity among nodes …

Network embedding is a promising field and is important for various network analysis tasks,
such as link prediction, node classification, community detection and others. Most research …

Link prediction methods use patterns in known network data to infer which connections may
be missing. Previous work has shown that continuous-time quantum walks can be used to …

Link prediction is an essential research area in network analysis. Based on the technique of
matrix completion, an algorithm for link prediction in networks is proposed. We propose a …

A Survey of the Link Prediction on Static and Temporal Knowledge Graph | Journal on
Information Technologies & Communications Research and Development on Information and …