Results 31 to 40 of about 138,699 (301)
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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Network Embedding For Brain Connectivity [PDF]
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021 In Neurosciences, networks are currently used for representing the brain connections system with the purpose of determining the specific characteristics of the brain itself. However, discriminating between a healthy human brain network and a pathological one using common network descriptors could be misleading.
Carboni, Lucrezia +2 more
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Computing the nearest euclidean distance matrix with low embedding dimensions [PDF]
, 2013 Euclidean distance embedding appears in many high-profile applications including wireless sensor network localization, where not all pairwise distances among sensors are known or accurate.
Qi, Hou-Duo, Yuan, Xiaoming, Qi, Hou Duo
core +1 more source
A Tutorial on Network Embeddings
CoRR, 2018 Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization.
Haochen Chen +3 more
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Zoo guide to network embedding
Journal of Physics: Complexity, 2023 Abstract Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has ...
Baptista, A +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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Network Embedding: An Overview
CoRR, 2019 Networks are one of the most powerful structures for modeling problems in the real world. Downstream machine learning tasks defined on networks have the potential to solve a variety of problems. With link prediction, for instance, one can predict whether two persons will become friends on a social network.
Nino Arsov, Georgina Mirceva
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Compositional Network Embedding
CoRR, 2019 Accepted By RecSys ...
Tianshu Lyu +4 more
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Gaussian Embedding of Temporal Networks
IEEE Access, 2023 Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuous-time relational data with timestamped interactions introduces unique challenges due to its sparsity.
Raphaël Romero +4 more
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