This paper aims to provide a comprehensive critical overview on how entities and their
interactions in Complex Networked Systems (CNS) are modelled across disciplines as they …

Computational drug repositioning and drug-target prediction have become essential tasks in
the early stage of drug discovery. In previous studies, these two tasks have often been …

Link prediction in attributed networks has attracted increasing attention recently due to its
valuable real-world applications. Various related methods have been proposed, but most of …

The ability to faithfully represent real social networks is critical from the perspective of testing
various what-if scenarios which are not feasible to be implemented in a real system as the …

Many real-world networks belong to the kind that evolves over time. So it is very meaningful
and challenging to predict whether the link will occur in the network of future time. In this …

Dynamic link prediction is essential for a wide range of domains, including social networks,
bioinformatics, knowledge bases, and recommender systems. Existing works have …

We consider the problem of embedding a dynamic network, to obtain time-evolving vector
representations of each node, which can then be used to describe changes in behaviour of …

Temporal link prediction (TLP) aims to predict future links and is attracting increasing
attention. The diverse interaction patterns and nonlinear nature of temporal networks make it …

Temporal link prediction on dynamic graphs is essential to various areas such as
recommendation systems, social networks, and citation analysis, and thus attracts great …

Temporal network link prediction is an important task in the field of network science, and has
a wide range of applications in practical scenarios. Revealing the evolutionary mechanism …