As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a
profound fusion with novel communication paradigms and emerging technological trends …

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute
an extensively-researched machine learning sub-field for the creation of synthetic data …

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

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 …

This work formalizes the associational task of predicting node attribute evolution in temporal
graphs from the perspective of learning equivariant representations. We show that node …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

The traffic state in an urban transportation network is determined via spatio-temporal traffic
propagation. In early traffic forecasting studies, time-series models were adopted to …

With the emergence of ever-advancing network threats, the guarantee of system security
becomes increasingly crucial, especially in the dynamic and decentralized ad-hoc networks …

In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …