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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
A Survey on Network Embedding [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2019 Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and
Jian Pei, Xiao Wang, Wenwu Zhu
exaly +4 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
Dynamic network embedding survey [PDF]
Neurocomputing, 2022 Neurocomputing ...
Ming Zhong, Jia Chen, Jianxin Li
exaly +4 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
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
Method of Attributed Heterogeneous Network Embedding with Multiple Features [PDF]
Jisuanji kexue, 2022 Network embedding aims to represent nodes in unstructured network with low-dimensional,real-valued vectors,so that node embedding can retain the structural and attribute features of the original network as much as possible.However,current research mainly
TANG Qi-you, ZHANG Feng-li, WANG Rui-jin, WANG Xue-ting, ZHOU Zhi-yuan, HAN Ying-jun
doaj +1 more source
Attribute Network Representation Learning Based on Global Attention [PDF]
Jisuanji kexue, 2021 The attribute network not only has complex topology,its nodes also contain rich attribute information.Attribute network represent learning methods simultaneously extracts network topology and node attribute information to learn low-dimensional vector ...
XU Ying-kun, MA Fang-nan, YANG Xu-hua, YE Lei
doaj +1 more source
Network Alignment with Holistic Embeddings [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different
Thanh Trung Huynh +6 more
openaire +6 more sources