Abstract Information cascades are important dynamical processes in complex networks. An
information cascade can describe the spreading dynamics of rumour, disease, memes, or …

Network science offers powerful tools to model complex social systems. Most social network
science research focuses on topological networks by simply considering the binary state of …

Many large-scale machine learning problems (such as clustering, non-parametric learning,
kernel machines, etc.) require selecting, out of a massive data set, a manageable …

Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …

Many large-scale machine learning problems–clustering, non-parametric learning, kernel
machines, etc.–require selecting a small yet representative subset from a large dataset …

Studying the structure and evolution characteristics of social networks is of great significance
in assessing and controlling the outbreak of infectious diseases. Therefore, it is necessary to …

Many real-world networks are embedded in spaces. Recent studies have found that spatial
characteristics are closely related to network features. Bus transport networks (BTNs) are …

Immunization of complex network with minimal or limited budget is a challenging issue for
research community. In spite of much literature in network immunization, no comprehensive …

In social influence analysis, viral marketing, and other fields, the influence maximization
problem is a fundamental one with critical applications and has attracted many researchers …

The problem of finding a better immunization strategy for controlling the spreading of the
epidemic with limited resources has attracted much attention because of its great theoretical …