Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …

The integration of deep learning (DL) and fuzzy learning (FL) is considered a recently
emerging and promising research direction in data embedding. The integrated fuzzy neural …

To combat cybercrimes and maintain financial security for the blockchain ecosystem,“know
your customer”(KYC) is an essential and also challenging process due to the pseudonymity …

The rapid development of community detection algorithms, while serving users in social
networks, also brings about certain privacy problems. In this work, we study community …

Temporal link prediction (TLP) is an inference task on dynamic graphs that predicts future
topology using historical graph snapshots. Existing TLP methods are usually designed for …

Exploring dynamic patterns from complex and large-scale networks is a significant and
challenging task in graph analysis. One of the most advanced solutions is dynamic graph …

Cement has been widely used in civil engineering directly and plays a critical role in cement-
based materials, eg, concrete. As the microstructural evolution of cement hydration …

Recent studies on heterogeneous information network (HIN) embedding-based
recommendations have encountered challenges. These challenges are related to the data …

As a fundamental problem in network science, link prediction is both theoretically significant
and practically useful. Many existing link prediction algorithms rely on predefined …