Results 261 to 270 of about 8,419,643 (305)

Motif-Preserving Dynamic Attributed Network Embedding

The Web Conference, 2021
Network embedding has emerged as a new learning paradigm to embed complex network into a low-dimensional vector space while preserving node proximities in both network structures and properties. It advances various network mining tasks, ranging from link
Zhijun Liu, Chao Huang, Yanwei Yu, Junyu Dong   +3 more
semanticscholar   +1 more source

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism

IEEE Transactions on Neural Networks and Learning Systems, 2021
Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks.
Pengfei Jiao, Xuan Guo, Xin Jing, Dongxiao He, Huamin Wu, Shirui Pan, Maoguo Gong, Wenjun Wang   +7 more
semanticscholar   +1 more source

A network embedding framework based on integrating multiplex network for drug combination prediction

Briefings Bioinform., 2021
Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive.
Liang Yu, Mingfei Xia, Qi An
semanticscholar   +1 more source

Multi-task network embedding

International Journal of Data Science and Analytics, 2017
As there are various data mining applications involving network analysis, network embedding is frequently employed to learn latent representations or embeddings that encode the network structure. However, existing network embedding models are only designed for a single network scenario.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu   +3 more
openaire   +2 more sources

Domain Adaptive Network Embedding

IEEE Transactions on Big Data, 2022
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph-structured data.
Guojie Song, Yizhou Zhang, Lingjun Xu, Haibing Lu   +3 more
semanticscholar   +1 more source

RDAM: A Reinforcement Learning Based Dynamic Attribute Matrix Representation for Virtual Network Embedding

IEEE Transactions on Emerging Topics in Computing, 2021
Network virtualization makes it possible to manage multiple virtual networks simultaneously on substrate physical networks. Virtual network embedding (VNE) is the critical step of network virtualization that maps virtual network requests to substrate ...
Haipeng Yao, Bo Zhang, Peiying Zhang, Sheng Wu, Chunxiao Jiang, Song Guo   +5 more
semanticscholar   +1 more source

Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks

IEEE Journal on Selected Areas in Communications, 2020
Virtual network embedding arranges virtual network services onto substrate network components. The performance of embedding algorithms determines the effectiveness and efficiency of a virtualized network, making it a critical part of the network ...
Zhongxia Yan, Jingguo Ge, Yulei Wu, Liangxiong Li, Tong Li   +4 more
semanticscholar   +1 more source

Deep Attributed Network Embedding by Preserving Structure and Attribute Information

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
Network embedding aims to learn distributed vector representations of nodes in a network. The problem of network embedding is fundamentally important. It plays crucial roles in many applications, such as node classification, link prediction, and so on ...
Richang Hong, Yuan He, Le Wu, Yong Ge, Xindong Wu   +4 more
semanticscholar   +1 more source

Amer: A New Attribute-Missing Network Embedding Approach

IEEE Transactions on Cybernetics, 2022
Network embedding which aims to learn a low dimensional representation of nodes is a powerful technique for network analysis. While network embedding for networks with complete attributes has been widely investigated, in many real-world applications the ...
Di Jin, Rui Wang, Tao Wang, Dongxiao He, Weiping Ding, Yuxiao Huang, Longbiao Wang, W. Pedrycz   +7 more
semanticscholar   +1 more source

ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks

Knowledge Discovery and Data Mining, 2021
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have shown promising performance on many network analysis tasks.
Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin   +4 more
semanticscholar   +1 more source