A survey on embedding dynamic graphs
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 …
analytics and inference, supporting applications like node classification, link prediction, and …
An overview on the application of graph neural networks in wireless 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 …
have been widely applied in wireless networks and achieved impressive performance. To …
Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …
recommender systems and epidemiology. Representing complex networks as structures …
On the equivalence between temporal and static equivariant graph representations
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 …
graphs from the perspective of learning equivariant representations. We show that node …
Reinforced neighborhood selection guided multi-relational graph neural networks
Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …
various structured graph data, typically through message passing among nodes by …
GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction
J Chen, X Wang, X Xu - Applied Intelligence, 2022 - Springer
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 …
applications in biology, sociology, economy and industry. However, it is a challenge since …
Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)
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 …
propagation. In early traffic forecasting studies, time-series models were adopted to …
IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks
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 …
becomes increasingly crucial, especially in the dynamic and decentralized ad-hoc networks …
Generative adversarial networks for spatio-temporal data: A survey
Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …
Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation
Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute
an extensively-researched machine learning sub-field for the creation of synthetic data …
an extensively-researched machine learning sub-field for the creation of synthetic data …