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
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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
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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
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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
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Attributed Social Network Embedding [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2018 12 pages, 7 ...
Lizi Liao, Xiangnan He, Hanwang Zhang
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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
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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
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Hierarchical label with imbalance and attributed network structure fusion for network embedding
AI Open, 2022 Network embedding (NE) aims to learn low-dimensional vectors for nodes while preserving the network’s essential properties (e.g., attributes and structure).
Shu Zhao +4 more
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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
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Constrained Consistency Modeling for Attributed Network Embedding
IEEE Access, 2019 Network embedding has emerged as a fundamental approach to network analysis tasks. Its main purpose is to learn a suitable mapping function to convert nodes in networks into a low-dimensional representations.
Xuan Zang +3 more
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