Network embedding, aiming to learn the low-dimensional representations of nodes in
networks, is of paramount importance in many real applications. One basic requirement of …

To meet the requirement of social influence analytics in various applications, the problem of
influence maximization has been studied in recent years. The aim is to find a limited number …

Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

Background Due in part to declining vaccination rates, in 2018 over 20 states reported at
least one case of measles, and over 40,000 cases were confirmed in Europe. Anti-vaccine …

Many community detection algorithms have been developed to uncover the mesoscopic
properties of complex networks. However how good an algorithm is, in terms of accuracy …

Since initial reports regarding the impact of motion artifact on measures of functional
connectivity, there has been a proliferation of participant-level confound regression methods …

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning,
conventional GNNs struggle to break through the upper limit of the expressiveness of first …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

In multicellular organisms, dedicated regulatory circuits control cell type diversity and
responses. The crosstalk and redundancies within these circuits and substantial cellular …