Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview
Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests
from both industrial and academic communities. With its rapid development, the …
from both industrial and academic communities. With its rapid development, the …
Anomaly detection in dynamic graphs via transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide
applications in social networks, e-commerce, and cybersecurity. Recent deep learning …
applications in social networks, e-commerce, and cybersecurity. Recent deep learning …
Graph transfer learning via adversarial domain adaptation with graph convolution
This paper studies the problem of cross-network node classification to overcome the
insufficiency of labeled data in a single network. It aims to leverage the label information in a …
insufficiency of labeled data in a single network. It aims to leverage the label information in a …
RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection
D Guo, Z Liu, R Li - Neural Networks, 2023 - Elsevier
Due to the wide application of dynamic graph anomaly detection in cybersecurity, social
networks, e-commerce, etc., research in this area has received increasing attention. Graph …
networks, e-commerce, etc., research in this area has received increasing attention. Graph …
Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods
Q Zhang, M Yang, P Zhang, B Wu… - Cancer Biology & …, 2023 - pmc.ncbi.nlm.nih.gov
Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of
cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process …
cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process …
Dynamic link prediction by learning the representation of node-pair via graph neural networks
H Dong, L Li, D Tian, Y Sun, Y Zhao - Expert Systems with Applications, 2024 - Elsevier
Many real-world networks are dynamic, whose structure keeps changing over time. Link
prediction, which can foretell the emergence of future links, is one crucial task in dynamic …
prediction, which can foretell the emergence of future links, is one crucial task in dynamic …
Deep autoencoder architecture with outliers for temporal attributed network embedding
Temporal attributed network embedding aspires to learn a low-dimensional vector
representation for each node in each snapshot of a temporal network, which can be capable …
representation for each node in each snapshot of a temporal network, which can be capable …
[HTML][HTML] Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings
This article proposes a novel deep clustering model based on the variational autoencoder
(VAE), named GamMM-VAE, which can learn latent representations of training data for …
(VAE), named GamMM-VAE, which can learn latent representations of training data for …
A method for evaluating the learning concentration in head-mounted virtual reality interaction
Y Lin, Y Lan, S Wang - Virtual Reality, 2023 - Springer
In education, learning concentration is closely related to the quality of learning, and teachers
can adjust their teaching methods accordingly to improve the learning outcomes of students …
can adjust their teaching methods accordingly to improve the learning outcomes of students …
Dynamic graph representation learning via coupling-process model
P Duan, C Zhou, Y Liu - IEEE Transactions on Neural Networks …, 2023 - ieeexplore.ieee.org
Representation learning based on dynamic graphs has received a lot of attention in recent
years due to its wide range of application scenarios. Although many discrete or continuous …
years due to its wide range of application scenarios. Although many discrete or continuous …