Omnipresent network/graph data generally have the characteristics of nonlinearity,
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …

Link prediction in complex networks has attracted considerable attention from
interdisciplinary research communities, due to its ubiquitous applications in biological …

Influence maximization is an important technique for its significant value on various social
network applications, such as viral marketing, advertisement, and recommendation …

In recent years, multiplex network embedding has received great attention from researchers.
However, existing multiplex network embedding methods neglect structural role information …

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

With the fast development of online social networks, a large number of their members are
involved in more than one social network. Finding most influential users is one of the …

Network embedding is a fundamental part of many network analysis tasks, including node
classification and link prediction. The existing random walk-based embedding methods aim …

Role-based network embedding aims to map network into low-dimensional node
representations while preserving structural similarities. Adjacency matrix contain both the …

For intelligent tutoring systems, Cognitive Diagnosis (CD) is a fundamental task that aims to
estimate the mastery degree of a student on each skill according to the exercise record. The …

Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the
aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS) …