Results 31 to 40 of about 8,419,643 (305)
Embedding-aided network dismantling
Physical Review Research, 2023 Optimal percolation concerns the identification of the minimum-cost strategy for the destruction of any extensive connected components in a network. Solutions of such a dismantling problem are important for the design of optimal strategies of disease ...
Saeed Osat +3 more
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A Link Stress-related Virtual Network Embedding Algorithm [PDF]
Jisuanji gongcheng, 2016 Aiming at the problem of high link stress in substrate network caused by traditional Virtual Network Embedding(VNE) algorithm,a new VNE algorithm is proposed.In the stage of node embedding,the importance degree of node in network is gotten by its ...
ZHANG Jingjing,ZHAO Chenggui,YUAN Jianming
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SINE: Second-Order Information Network Embedding
IEEE Access, 2020 As an important data type, the demand of network analysis and learning is increasingly prominent. A key problem of network analysis is to study how to reasonably represent the feature information in the network, that is network embedding. However, in the
Ziqi Wang +3 more
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Heterogeneous Information Network Representation Learning Incorporating Community Structure
IEEE Access, 2022 Network embedding usually learns the node representations using their local context information. However, as an important mesoscopic description of network structures, community structures hidden in the networks have been largely ignored.
Wei Yu +5 more
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Context Embedding Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual.
Kim, Kun Ho +2 more
openaire +3 more sources
Unsupervised Attributed Multiplex Network Embedding [PDF]
AAAI Conference on Artificial Intelligence, 2019 Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes.
Chanyoung Park +3 more
semanticscholar +1 more source
Directed Community Detection With Network Embedding
Journal of the American Statistical Association, 2021 Community detection in network data aims at grouping similar nodes sharing certain characteristics together. Most existing methods focus on detecting communities in undirected networks, where similarity between nodes is measured by their node features ...
Jingnan Zhang, Xin He, Junhui Wang
semanticscholar +1 more source
REFINE: Random RangE FInder for Network Embedding [PDF]
International Conference on Information and Knowledge Management, 2021 Network embedding approaches have recently attracted considerable interest as they learn low-dimensional vector representations of nodes. Embeddings based on the matrix factorization are effective but they are usually computationally expensive due to the
Hao Zhu, Piotr Koniusz
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AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding [PDF]
, 2018 Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and ...
Qian, Shengsheng +3 more
core +1 more source
Community Preserving Network Embedding
AAAI Conference on Artificial Intelligence, 2017 Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the ...
Xiao Wang +5 more
semanticscholar +1 more source