A survey on embedding dynamic graphs

CDT Barros, MRF Mendonça, AB Vieira… - ACM Computing Surveys …, 2021 - dl.acm.org
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 …

An overview on the application of graph neural networks in wireless networks

S He, S Xiong, Y Ou, J Zhang, J Wang… - IEEE Open Journal …, 2021 - ieeexplore.ieee.org
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 …

Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey

J Skarding, B Gabrys, K Musial - iEEE Access, 2021 - ieeexplore.ieee.org
Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

On the equivalence between temporal and static equivariant graph representations

J Gao, B Ribeiro - International Conference on Machine …, 2022 - proceedings.mlr.press
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 …

Reinforced neighborhood selection guided multi-relational graph neural networks

H Peng, R Zhang, Y Dou, R Yang, J Zhang… - ACM Transactions on …, 2021 - dl.acm.org
Graph Neural Networks (GNNs) have been widely used for the representation learning of
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 …

Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

B Yu, Y Lee, K Sohn - Transportation research part C: emerging …, 2020 - Elsevier
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 …

IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks

S Huang, K Lei - Ad Hoc Networks, 2020 - Elsevier
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 …

Generative adversarial networks for spatio-temporal data: A survey

N Gao, H Xue, W Shao, S Zhao, KK Qin… - ACM Transactions on …, 2022 - dl.acm.org
Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

H Navidan, PF Moshiri, M Nabati, R Shahbazian… - Computer Networks, 2021 - Elsevier
Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute
an extensively-researched machine learning sub-field for the creation of synthetic data …