Link prediction is a paradigmatic problem in network science, which aims at estimating the
existence likelihoods of nonobserved links, based on known topology. After a brief …

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 …

The identification of influential spreaders in complex networks is a popular topic in studies of
network characteristics. Many centrality measures have been proposed to address this …

Link prediction provides methods for estimating potential connections in complex networks
that have theoretical and practical relevance for personalized recommendations and various …

Many link prediction algorithms have originated from the process of information transmission
between nodes in recent years. Despite these algorithms can obtain great prediction results …

Real-world networks are composed of diverse interacting and evolving entities, while most
of existing researches simply characterize them as particular static networks, without …

In social topics, the study of rumor propagation mechanisms is essential to master social
behavior. In this paper, aiming to explore the adversarial game relationship between rumor …

In recent years, multiplex networks have been introduced to describe real complex systems,
where the same group of entities make different types of interaction. In a multiplex network …

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 …

Identifying information sources plays a significant role in network science and engineering.
However, existing source identification approaches generally focus on static networks …