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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
Peng Cui, Xiao Wang, Jian Pei
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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
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Pattern Recognition, 2019
Abstract In network embedding, random walks play a fundamental role in preserving network structures. However, random walk methods have two limitations. First, they are unstable when either the sampling frequency or the number of node sequences changes.
Yong Shi 0001 +3 more
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Abstract In network embedding, random walks play a fundamental role in preserving network structures. However, random walk methods have two limitations. First, they are unstable when either the sampling frequency or the number of node sequences changes.
Yong Shi 0001 +3 more
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Data Mining and Knowledge Discovery, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Min Shi 0001 +4 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Min Shi 0001 +4 more
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Embeddings of circulant networks
Journal of Combinatorial Optimization, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Indra Rajasingh +3 more
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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 +3 more
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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 +3 more
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Spectral embedding of directed networks
Social Network Analysis and Mining, 2015Most relationships in a social network are asymmetric: The strength of A’s relationship to B is not the same as the strength of B’s relationship to A. Such relationships can reflect asymmetric emotional bonds, influence or power. It is natural to model such social networks by directed graphs, with a node for each participant, and a weighted directed ...
Quan Zheng 0001, David B. Skillicorn
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A networked haptic embedded controller
Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014), 2014Recent innovation in embedded computing systems has allowed a new generation of smart devices and home appliances, such as tablets, smartphones and smartTVs, with embedded complete computing and networking capabilities for a more intuitive and functional operation.
Carlo Alberto Avizzano +3 more
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Extractive Adversarial Networks for Network Embedding
2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC), 2018Network embedding has attracted more and more researchers recently. Although many algorithms focus on topological information, there exists a disadvantage. Nodes in many real-world network often have their own attributes, which are potentially valuable information, but algorithms focusing on structure ignore these messages, which will decrease the ...
Runxuan Chen +5 more
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ACM Transactions on Knowledge Discovery from Data, 2021
Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification [ 16 , 31 , 42 ]. Most existing methods leverage multi-similarities between nodes such as connectivity, which considers vertices that ...
Ping Shao +3 more
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Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification [ 16 , 31 , 42 ]. Most existing methods leverage multi-similarities between nodes such as connectivity, which considers vertices that ...
Ping Shao +3 more
openaire +1 more source