Results 241 to 250 of about 170,883 (277)
Node2Vec-DGI-EL: a hierarchical graph representation learning model for ingredient-disease association prediction. [PDF]
Bioinform AdvZhang L, Dong X, Jia S, Zhang J.
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Human cortical dynamics reflect graded contributions of local geometry and network topography. [PDF]
Nat CommunRoyer J +14 more
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Cross-Species Aging Knowledge Integration into Agentic AI Platform Uncovers Conserved Mechanisms
Ahuja G +20 more
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Node embedding with capsule generation-embedding network
International Journal of Machine Learning and Cybernetics, 2023Jinghong Wang, Jianguo Wei
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On Node Embedding of Uncertain Networks
2020 IEEE International Conference on Big Data (Big Data), 2020Node embedding has recently shown state-of-the-art performance in various network analysis tasks. However, most of the existing node embedding methods do not consider the uncertainty of the input data, which is often the case in practice. This work offers an empirical evaluation of the typical node embedding methods when applied on uncertain networks ...
Hoang H Nguyen +2 more
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From Node Embeddings to Triple Embeddings. [PDF]
, 2021 An extended version of this paper has been published at the the 34th AAAI Conference on Artificial Intelligence (AAAI) with the title “Learning Triple Embeddings from Knowledge Graphs”. Graph embedding techniques allow to learn high-quality low-dimensional graph representations useful in various tasks, from node classification to clustering.
Fionda Valeria, Pirro' Giuseppe
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Analyzing Centralities of Embedded Nodes
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018Given a dataset described as a graph such as social networks, node embedding algorithms estimate a real-valued vector for each node that can later be used for a machine learning task such as node classification. These embedding vectors simplify the task and often improve the task performance.
Kento Nozawa +2 more
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Hyperbolic node embedding for temporal networks
Data Mining and Knowledge Discovery, 2021Generating general-purpose vector representations of networks allows us to analyze them without the need for extensive feature-engineering. Recent works have shown that the hyperbolic space can naturally represent the structure of networks, and that embedding networks into hyperbolic space is extremely efficient, especially in low dimensions.
Lili Wang +4 more
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LatinX in AI at Neural Information Processing Systems Conference 2018, 2018
Node embedding (NE) algorithms capture features of graph’s nodes and represent them in a low dimensional vector space. Graphs are inherently noisy structures, which might reduce the learned representations quality. We propose a novel approach using denoising autoencoders to reduce noise in the learned representation of nodes.
Dehua Chen +3 more
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Node embedding (NE) algorithms capture features of graph’s nodes and represent them in a low dimensional vector space. Graphs are inherently noisy structures, which might reduce the learned representations quality. We propose a novel approach using denoising autoencoders to reduce noise in the learned representation of nodes.
Dehua Chen +3 more
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Ring Embedding in Hypercubes with Faculty Nodes
Parallel Processing Letters, 1997Hypercube is an attractive structure for parellel processing due to its symmetry and regularity. To increase the reliability of hypercube based systems and to allow their use in the presence of faulty nodes, efficient fault-tolerant schemes in hypercubes are necessary.
Kim, J.S., Maeng, S.R., Yoon, H.
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