Community detection in social networks has become a popular topic of research during the
last decade. There exist a variety of algorithms for modularizing the network graph into …

As a hot research topic in network science, community detection has attracted much
attention of scholars. In recent years, many methods have emerged to discover the …

Contagion processes are strongly linked to the network structures on which they propagate,
and learning these structures is essential for understanding and intervention on complex …

When information or infectious diseases spread over a network, in many practical cases,
one can observe when nodes adopt information or become infected, but the underlying …

An explicit network structure plays an important role when analyzing and understanding
diffusion processes. In many scenarios, however, the interactions between nodes in an …

Information diffusion, spreading of infectious diseases, and spreading of rumors are
fundamental processes occurring in real-life networks. In many practical cases, one can …

Using observational data to infer the coupling structure or parameters in dynamical systems
is important in many real-world applications. In this paper, we propose a framework of …

Numerous algorithms have been proposed to infer the underlying structure of the social
networks via observed information propagation. The previously proposed algorithms …

Access to complete data in large-scale networks is often infeasible. Therefore, the problem
of missing data is a crucial and unavoidable issue in the analysis and modeling of real-world …

Influence maximization has commonly been studied in the context of strategically allocating
resources to agents in a network to maximize the spread of an opinion. In the first part of this …