Results 1 to 10 of about 138,699 (301)
Virtual Network Embedding: A Survey
IEEE Communications Surveys and Tutorials, 2013 Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change.
Andreas Fischer +2 more
exaly +5 more sources
DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning [PDF]
Scientific Reports, 2023 Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals.
Xiancui Xiao
doaj +2 more sources
Proximity-Based Compression for Network Embedding [PDF]
Frontiers in Big Data, 2021 Network embedding that encodes structural information of graphs into a low-dimensional vector space has been proven to be essential for network analysis applications, including node classification and community detection.
Muhammad Ifte Islam +4 more
doaj +2 more sources
Discrete Network Embedding [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and ...
Liu, Weiwei +4 more
core +2 more sources
Network embedding: Taxonomies, frameworks and applications
Computer Science Review, 2020 Networks are a general language for describing complex systems of interacting entities. In the real world, a network always contains massive nodes, edges and additional complex information which leads to high complexity in computing and analyzing tasks ...
Mingliang Hou, Jing Ren, Da Zhang
exaly +3 more sources
Deep Dynamic Network Embedding for Link Prediction
IEEE Access, 2018 Network embedding task aims at learning low-dimension latent representations of vertices while preserving the structure of a network simultaneously. Most existing network embedding methods mainly focus on static networks, which extract and condense the ...
Taisong Li +4 more
doaj +3 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
Full-network embedding in a multimodal embedding pipeline [PDF]
CoRR, 2017 The current state-of-the-art for image annotation and image retrieval tasks is obtained through deep neural networks, which combine an image representation and a text representation into a shared embedding space.
Ayguadé Parra, Eduard +7 more
core +6 more sources
Dynamic network embedding survey [PDF]
Neurocomputing, 2022 Dynamic network embedding ...
G Xue (13400385) +5 more
core +3 more sources
Attributed social network embedding [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2017 Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval.
Zhang, Hanwang +7 more
core +5 more sources