Semidual Kitaev lattice model and tensor network representation
Sign Up to like & getrecommendations! Published in 2021 at "Journal of High Energy Physics"
DOI: 10.1007/jhep09(2021)210
Abstract: Abstract Kitaev’s lattice models are usually defined as representations of the Drinfeld quantum double D(H) = H ⋈ H*op, as an example of a double cross product quantum group. We propose a new version based… read more here.
Keywords: tensor network; network representation; model; kitaev lattice ... See more keywords
MVTree for Hierarchical Network Representation Based on Geometric Algebra Subspace
Sign Up to like & getrecommendations! Published in 2018 at "Advances in Applied Clifford Algebras"
DOI: 10.1007/s00006-018-0855-x
Abstract: Most hierarchical representation methods are designed from engineering perspectives, lacking an appropriate mathematical foundation to integrate different problem definitions. To solve this problem, a hierarchical network representation model based on geometric algebra (GA) subspace is… read more here.
Keywords: network; based geometric; network representation; hierarchical network ... See more keywords
DHNE: Network Representation Learning Method for Dynamic Heterogeneous Networks
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2942221
Abstract: Analyzing the rich information behind heterogeneous networks through network representation learning methods is signifcant for many application tasks such as link prediction, node classifcation and similarity research. As the networks evolve over times, the interactions… read more here.
Keywords: dynamic heterogeneous; heterogeneous networks; network representation; representation learning ... See more keywords
Prediction of Drug–Target Interactions Based on Network Representation Learning and Ensemble Learning
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2020.2989765
Abstract: Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug–protein interactions (positive… read more here.
Keywords: negative samples; drug; drug protein; prediction ... See more keywords
A Comparative Study for Unsupervised Network Representation Learning
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2019.2951398
Abstract: There has been significant progress in unsupervised network representation learning (UNRL) approaches over graphs recently with flexible random-walk approaches, new optimization objectives, and deep architectures. However, there is no common ground for systematic comparison of… read more here.
Keywords: representation learning; unsupervised network; network representation; study ... See more keywords
Signed Network Representation by Preserving Multi-Order Signed Proximity
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3125148
Abstract: Signed network representation is a key problem for signed network data. Previous studies have shown that by preserving multi-order signed proximity (SP), expressive node representations can be learned. However, multi-order SP cannot be perfectly encoded… read more here.
Keywords: network representation; multi order; signed network; order ... See more keywords
Network representation learning method embedding linear and nonlinear network structures
Sign Up to like & getrecommendations! Published in 2022 at "Semantic Web"
DOI: 10.3233/sw-212968
Abstract: With the rapid development of neural networks, much attention has been focused on network embedding for complex network data, which aims to learn low-dimensional embedding of nodes in the network and how to effectively apply… read more here.
Keywords: representation; learning method; representation learning; network ... See more keywords
Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2022.899340
Abstract: Identifying biomarkers of Multiple Sclerosis is important for the diagnosis and treatment of Multiple Sclerosis. The existing study has shown that miRNA is one of the most important biomarkers for diseases. However, few existing methods… read more here.
Keywords: network representation; sclerosis; mirnas associated; multiple sclerosis ... See more keywords
An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks
Sign Up to like & getrecommendations! Published in 2021 at "Mathematics"
DOI: 10.3390/math9151767
Abstract: Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods… read more here.
Keywords: representation learning; information; network; network representation ... See more keywords
Attribute Network Representation Learning with Dual Autoencoders
Sign Up to like & getrecommendations! Published in 2022 at "Symmetry"
DOI: 10.3390/sym14091840
Abstract: The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when… read more here.
Keywords: information; network; network representation; representation learning ... See more keywords