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
Generic multimodal spatially graph network for spatially embedded network representation learning
Sign Up to like & getrecommendations! Published in 2025 at "Applied Network Science"
DOI: 10.1007/s41109-025-00751-6
Abstract: Spatially embedded networks (SENs) represent a special type of complex graph, whose topologies are constrained by the networks’ embedded spatial environments. The graph representation of such networks is thereby influenced by the embedded spatial features… read more here.
Keywords: multimodal spatially; spatially embedded; representation; network representation ... 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
Enhanced Protein Network Representation with Explicit Structural Binding for Protein-Protein Interaction Prediction.
Sign Up to like & getrecommendations! Published in 2025 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2025.3632354
Abstract: Protein-protein interactions (PPIs) are fundamental molecular events in the human body, playing a pivotal role in disease treatment and intervention. However, existing approaches for protein representation often rely on simplistic PPI network models, which face… read more here.
Keywords: protein network; enhanced protein; representation; network representation ... 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-to-Network: Self-Supervised Network Representation Learning via Position Prediction
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2024.3493391
Abstract: Network Representation Learning (NRL) has achieved remarkable success in learning low-dimensional representations for network nodes. However, most NRL methods, including Graph Neural Networks (GNNs) and their variants, face critical challenges. First, labeled network data, which… read more here.
Keywords: self supervised; network representation; representation learning; position ... See more keywords
Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis
Sign Up to like & getrecommendations! Published in 2024 at "PLOS Computational Biology"
DOI: 10.1371/journal.pcbi.1012130
Abstract: Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling… read more here.
Keywords: network representation; analysis; functional connectivity; activity ... 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