13.3.4 Network Embedding, Graph Embedding

Chapter Contents (Back)
Network Embedding. Network embedding aims to convert network into continuous embedding, where each node corresponds to a vector.

Emms, D.[David], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Graph matching using the interference of continuous-time quantum walks,
PR(42), No. 5, May 2009, pp. 985-1002.
Elsevier DOI 0902
BibRef
Earlier:
Graph Edit Distance without Correspondence from Continuous-Time Quantum Walks,
SSPR08(5-14).
Springer DOI 0812
BibRef
Earlier:
Graph Embedding Using Quantum Commute Times,
GbRPR07(371-382).
Springer DOI 0706
Graph matching; Continuous-time quantum walk; Interference; Object recognition BibRef

Wacquet, G., Poisson Caillault, É., Hamad, D., Hébert, P.A.,
Constrained spectral embedding for K-way data clustering,
PRL(34), No. 9, July 2013, pp. 1009-1017.
Elsevier DOI 1305
Graph embedding; Spectral clustering; Pairwise constraints; Signed Laplacian BibRef

Li, J., Wu, Y., Zhao, J., Lu, K.,
Low-Rank Discriminant Embedding for Multiview Learning,
Cyber(47), No. 11, November 2017, pp. 3516-3529.
IEEE DOI 1710
Euclidean distance, Face, Kernel, Laplace equations, Manifolds, Robustness, Training, Graph embedding, low-rank representation (LRR), multiview learning, subspace, learning BibRef

Ran, Z.Y.[Zhi-Yong], Wang, W.[Wei], Hu, B.G.[Bao-Gang],
On connections between Rényi entropy Principal Component Analysis, kernel learning and graph embedding,
PRL(112), 2018, pp. 125-130.
Elsevier DOI 1809
Renyi entropy, PCA, Kernel learning, Graph embedding BibRef

Madi, K.[Kamel], Paquet, E.[Eric], Kheddouci, H.[Hamamache],
New graph distance for deformable 3D objects recognition based on triangle-stars decomposition,
PR(90), 2019, pp. 297-307.
Elsevier DOI 1903
Graph matching, Graph edit distance, Graph decomposition, Graph embedding, Graph metric, Graph classification, Metric learning BibRef

Lozano, M.A.[Miguel Angel], Escolano, F.[Francisco], Curado, M.[Manuel], Hancock, E.R.[Edwin R.],
Network embedding from the line graph: Random walkers and boosted classification,
PRL(143), 2021, pp. 36-42.
Elsevier DOI 2102
Network embedding, SGNS, Line graph, Spectral theory BibRef

Curado, M.[Manuel], Escolano, F.[Francisco], Lozano, M.A.[Miguel A.], Hancock, E.R.[Edwin R.],
Semi-supervised Graph Rewiring with the Dirichlet Principle,
ICPR18(2172-2177)
IEEE DOI 1812
Laplace equations, Electrical resistance measurement, Resistance, Estimation, Kernel, Pattern recognition, Computer science BibRef

Wang, Y.Y.[Yue-Yang], Duan, Z.H.[Zi-Heng], Huang, Y.[Yida], Xu, H.Y.[Hao-Yan], Feng, J.[Jie], Ren, A.N.[An-Ni],
MTHetGNN: A heterogeneous graph embedding framework for multivariate time series forecasting,
PRL(153), 2022, pp. 151-158.
Elsevier DOI 2201
Multivariate time series forecasting, Graph neural networks, Heterogeneous graph embedding BibRef

Bai, L.[Liang], Liang, J.[Jiye],
A categorical data clustering framework on graph representation,
PR(128), 2022, pp. 108694.
Elsevier DOI 2205
Cluster analysis, Categorical data clustering, Data representation, Graph embedding BibRef

Wang, S.L.[Shu-Liang], Qin, X.R.[Xiao-Rui], Chi, L.H.[Lian-Hua],
HashWalk: An efficient node classification method based on clique-compressed graph embedding,
PRL(156), 2022, pp. 133-141.
Elsevier DOI 2205
Node classification, Clique, Graph embedding, Random walk BibRef

Jiang, H.[Hua], Xiao, B.[Bing], Luo, Y.[Yintao], Ma, J.L.[Jun-Liang],
A self-attentive model for tracing knowledge and engagement in parallel,
PRL(165), 2023, pp. 25-32.
Elsevier DOI 2301
Knowledge tracing, Graph embedding, Self-attention, Massive open online courses, Deep learning, Engagement BibRef

Shen, X.B.[Xiao-Bo], Ong, Y.S.[Yew-Soon], Mao, Z.[Zheng], Pan, S.R.[Shi-Rui], Liu, W.W.[Wei-Wei], Zheng, Y.H.[Yu-Hui],
Compact network embedding for fast node classification,
PR(136), 2023, pp. 109236.
Elsevier DOI 2301
Network embedding, Hashing, Compact representation, Graph BibRef

Chen, D.D.[Dong-Dong], Dai, Y.X.[Yu-Xing], Zhang, L.[Lichi], Zhang, Z.H.[Zhi-Hong], Hancock, E.R.[Edwin R.],
Position-aware and structure embedding networks for deep graph matching,
PR(136), 2023, pp. 109242.
Elsevier DOI 2301
Graph Matching, Graph Embedding, Deep Neural Network BibRef

Ye, Z.L.[Zhi-Ling], Zhang, Z.H.[Zhi-Hong], Bai, L.[Lu], Hu, G.S.[Guo-Sheng], Bai, Z.J.[Zheng-Jian], Hu, Y.Q.[Yi-Qun], Hancock, E.R.[Edwin R.],
A Unified Neighbor Reconstruction Method for Embeddings,
ICPR18(3186-3191)
IEEE DOI 1812
Feature extraction, Manifolds, Sparse matrices, Reconstruction algorithms, Dimensionality reduction, Measurement, Computational modeling BibRef


Keetha, N.V.[Nikhil Varma], Wang, C.[Chen], Qiu, Y.H.[Yu-Heng], Xu, K.[Kuan], Scherer, S.[Sebastian],
AirObject: A Temporally Evolving Graph Embedding for Object Identification,
CVPR22(8397-8406)
IEEE DOI 2210

WWW Link. Location awareness, Semantics, Transforms, Encoding, Pattern recognition, Object recognition, Vision applications and systems BibRef

Xue, L.[Li], Yao, W.B.[Wen-Bin], Xia, Y.[Yamei], Li, X.Y.[Xiao-Yong],
Deep Attributed Network Embedding with Community Information,
MMMod21(I:653-665).
Springer DOI 2106
BibRef

Molokwu, B.C.[Bonaventure C.], Shuvo, S.B.[Shaon Bhatta], Kobti, Z.[Ziad], Kar, N.C.[Narayan C.],
Social Network Analysis using Knowledge-Graph Embeddings and Convolution Operations*,
ICPR21(6351-6358)
IEEE DOI 2105
Training, Social networking (online), Convolution, Predictive models, Feature extraction, Feature Extraction BibRef

Gao, X.[Xiyue], Chen, J.[Jun], Yao, J.[Jing], Zhu, W.Q.[Wen-Qian],
LDSNE: Learning Structural Network Embeddings by Encoding Local Distances,
MMMod20(I:642-652).
Springer DOI 2003
ow-dimensional features from the relationships and attributes of networks. BibRef

Schroeder, B., Tripathi, S., Tang, H.,
Triplet-Aware Scene Graph Embeddings,
SGRL19(1783-1787)
IEEE DOI 2004
data visualisation, graph theory, triplet supervision, data augmentation, scene graph representation, visualization, graph neural network BibRef

Do, K., Tran, T., Venkatesh, S.,
Knowledge Graph Embedding with Multiple Relation Projections,
ICPR18(332-337)
IEEE DOI 1812
data mining, graph theory, inference mechanisms, matrix algebra, statistical analysis, projection matrices, Computational modeling BibRef

Yang, S.[Shuang], Yang, B.[Bo],
Enhanced Network Embedding with Text Information,
ICPR18(326-331)
IEEE DOI 1812
Task analysis, Matrix decomposition, Linear programming, Pattern recognition, Computer science, Twitter BibRef

Zhang, L., Li, X., Xiang, J., Qi, Y.,
LHONE: Label Homophily Oriented Network Embedding,
ICPR18(665-670)
IEEE DOI 1812
Gaussian mixture model, Linear programming, Optimization, Social network services, Task analysis, Computational complexity BibRef

Harandi, M.T.[Mehrtash T.], Sanderson, C.[Conrad], Shirazi, S.A.[Sareh Abolahrari], Lovell, B.C.[Brian C.],
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching,
CVPR11(2705-2712).
IEEE DOI 1106
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Social Networks, Creation, Visualization, Use .


Last update:Jul 13, 2024 at 15:27:21