Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network [PDF]
Entropy, 2021 In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural ...
Wei Wu, Guangmin Hu, Fucai Yu
doaj +2 more sources
Effective attributed network embedding with information behavior extraction [PDF]
PeerJ Computer Science, 2022 Network embedding has shown its effectiveness in many tasks, such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attribute features to obtain a node embedding ...
Ganglin Hu, Jun Pang, Xian Mo
doaj +3 more sources
DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding [PDF]
iScience, 2021 Summary: Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments.
Bo-Ya Ji +4 more
doaj +2 more sources
Attributed network embedding based on self-attention mechanism for recommendation method [PDF]
Scientific Reports, 2023 Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems.
Shuo Wang, Jing Yang, Fanshu Shang
doaj +2 more sources
Protein complexes identification based on go attributed network embedding [PDF]
BMC Bioinformatics, 2018 Background Identifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of ...
Bo Xu +6 more
doaj +2 more sources
Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder
IEEE Access, 2022 Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications.
Amr Thabit Al-Furas +3 more
doaj +3 more sources
Automatic Controversy Detection Based on Heterogeneous Signed Attributed Network and Deep Dual-Layer Self-Supervised Community Analysis [PDF]
EntropyIn this study, we propose a computational approach that applies text mining and deep learning to conduct controversy detection on social media platforms.
Ying Li +3 more
doaj +2 more sources
Protein features fusion using attributed network embedding for predicting protein-protein interaction [PDF]
BMC GenomicsBackground Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design.
Mei-Yuan Cao +2 more
doaj +2 more sources
Enhancing Attributed Network Embedding via Similarity Measure
IEEE Access, 2019 Network embedding aims to represent network structural and attributed information with low-dimensional vectors, which has been demonstrated to be beneficial for many network analysis tasks, such as link prediction, node classification and visualization ...
Bin Yu +4 more
doaj +3 more sources
Recommendation algorithm based on attributed multiplex heterogeneous network [PDF]
PeerJ Computer Science, 2021 In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are ...
Zhisheng Yang, Jinyong Cheng
doaj +2 more sources