As a natural extension of link prediction on graphs, hyperlink prediction aims for the
inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than …

Rapid discovery of new reactions and molecules in recent years has been facilitated by the
advances in high throughput screening, accessibility to a highly complex chemical design …

Link prediction is a widely adopted method for extracting valuable data insights from graphs,
primarily aimed at predicting interactions between two nodes. However, there are not only …

Link prediction in bipartite networks finds practical applications in various domains,
including friend recommendation in social networks and chemical reaction prediction in …

Abstract Heterogeneous Information Networks (HINs) are commonly employed to model
complex real-world scenarios with diverse node and edge types. However, due to …

Link prediction, which aims to predict missing links or possible future links between two
nodes, is one of the most important research in social network analysis. Higher-order link …

Many real-world problems can be modelled in the form of complex networks. Social
networks such as research collaboration networks and facebook, biological neural networks …

Many real-world problems can be modelled in the form of complex networks. Social
networks such as research collaboration networks and facebook, biological neural networks …

This thesis explores graph-based approaches for prediction and similarity analysis problems
within networks and hypergraphs. While existing algorithms for link prediction in networks …

Rapid discovery of new reactions and molecules in recent years has been facilitated by the
advancements in high throughput screening, accessibility to a much more complex chemical …