IoT describes a new world of billions of objects that intelligently communicate and interact
with each other. One of the important areas in this field is a new paradigm-Social Internet of …

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 …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

Despite the prevalence of recent success in learning from static graphs, learning from time-
evolving graphs remains an open challenge. In this work, we design new, more stringent …

Dynamic network link prediction is becoming a hot topic in network science, due to its wide
applications in biology, sociology, economy and industry. However, it is a challenge since …

Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Predicting the potential relations between nodes in networks, known as link prediction, has
long been a challenge in network science. However, most studies just focused on link …

Heterogeneous information networks usually contain different kinds of nodes and
distinguishing types of relations, which can preserve more information than homogeneous …

In e-learning recommender systems, interpersonal information between learners is very
scarce, which makes it difficult to apply collaborative filtering (CF) techniques to achieve …

Several network embedding models have been developed for unsigned networks. However,
these models based on skip-gram cannot be applied to signed networks because they can …