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See also Multiple Line-Template Matching with the EM Algorithm.
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Coarse-to-Fine Dynamic Programming,
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0112
Dynamic Programming. Applied to mine detection.
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Cordella, L.P.,
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IEEE DOI
9808
BibRef
Cordella, L.P.,
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IEEE Abstract.
0409
BibRef
Earlier:
Fast Graph Matching for Detecting CAD Image Components,
ICPR00(Vol II: 1034-1037).
IEEE DOI
0009
BibRef
Earlier:
Performance evaluation of the VF graph matching algorithm,
CIAP99(1172-1177).
IEEE DOI
9909
BibRef
Earlier:
An Efficient Algorithm for the Inexact Matching of ARG Graphs
Using a Contextual Transformational Model,
ICPR96(III: 180-184).
IEEE DOI
9608
(Univ. di Napoli, I)
Earlier version worked on small and medium sized graphs. This works for large
graphs.
BibRef
Carletti, V.[Vincenzo],
Foggia, P.[Pasquale],
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Carletti, V.[Vincenzo],
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Introducing VF3: A New Algorithm for Subgraph Isomorphism,
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Algorithm design and analysis, Biology, Complexity theory,
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Multi-resolution segmentation
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Learning Graph Matching with a Graph-Based Perceptron in a
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GbRPR17(49-58).
Springer DOI
1706
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Elsevier DOI
0904
BibRef
Earlier:
Assessing the Performance of a Graph-Based Clustering Algorithm,
GbRPR07(215-227).
Springer DOI
0706
Benchmarking; Graph-based clustering; Cluster detection
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Massaro, A.[Alessio],
Pelillo, M.[Marcello],
Matching graphs by pivoting,
PRL(24), No. 8, May 2003, pp. 1099-1106.
Elsevier DOI
0304
BibRef
Earlier:
A Complementary Pivoting Approach to Graph Matching,
EMMCVPR01(469-479).
Springer DOI
0205
BibRef
Luo, B.[Bin],
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Eigenspaces For Graphs,
IJIG(2), No. 2, April 2002, pp. 247-268.
0204
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Wilson, R.C.[Richard C.],
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Spectral embedding of graphs,
PR(36No. 10, October 2003, pp. 2213-2230.
Elsevier DOI
0308
BibRef
Earlier:
Graph spectral approach for learning view structure,
ICPR02(III: 785-788).
IEEE DOI
0211
BibRef
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Hancock, E.R.,
Levenshtein distance for graph spectral features,
ICPR04(II: 489-492).
IEEE DOI
0409
BibRef
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BibRef
Earlier:
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ICIP03(II: 37-40).
IEEE DOI
0312
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Earlier:
Spectral Clustering of Graphs,
Springer DOI
0311
Generative model; Graph; Covariance matrix; Clustering
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ICPR14(3845-3850)
IEEE DOI
1412
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Earlier:
Entropic Graph Embedding via Multivariate Degree Distributions,
SSSPR14(163-172).
Springer DOI
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And:
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Springer DOI
1311
Complexity theory
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Han, L.[Lin],
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1509
Complexity theory
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From set of distances between graphs characterize pairwise affinity.
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Elsevier DOI
0512
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Earlier:
Spectral Simplification of Graphs,
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Springer DOI
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0506
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CIAP03(480-485).
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0310
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Projections onto Convex Sets.
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Bragg Diffraction Patterns as Graph Characteristics,
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Complexity Fusion for Indexing Reeb Digraphs,
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From Points to Nodes:
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BibRef
Escolano, F.[Francisco],
Hancock, E.R.[Edwin R.],
Lozano, M.A.[Miguel A.],
Graph Similarity through Entropic Manifold Alignment,
SIIMS(10), No. 2, 2017, pp. 942-978.
DOI Link
1708
BibRef
Earlier:
Skeletal Graphs from Schrödinger Magnitude and Phase,
GbRPR15(335-344).
Springer DOI
1511
BibRef
Earlier:
Graph matching through entropic manifold alignment,
CVPR11(2417-2424).
IEEE DOI
1106
BibRef
Escolano, F.[Francisco],
Bonev, B.[Boyan],
Lozano, M.A.[Miguel A.],
Information-Geometric Graph Indexing from Bags of Partial Node
Coverages,
GbRPR11(52-61).
Springer DOI
1105
BibRef
Bonev, B.[Boyan],
Escolano, F.[Francisco],
Giorgi, D.[Daniela],
Biasotti, S.[Silvia],
Information-theoretic Feature Selection from Unattributed Graphs,
ICPR10(930-933).
IEEE DOI
1008
BibRef
Escolano, F.[Francisco],
Lozano, M.A.[Miguel A.],
Bonev, B.[Boyan],
Suau, P.[Pablo],
Bypass information-theoretic shape similarity from non-rigid
points-based alignment,
NORDIA10(37-44).
IEEE DOI
1006
BibRef
Escolano, F.[Francisco],
Bonev, B.[Boyan],
Hancock, E.R.[Edwin R.],
Heat Flow-Thermodynamic Depth Complexity in Directed Networks,
SSSPR12(190-198).
Springer DOI
1211
BibRef
Escolano, F.[Francisco],
Curado, M.[Manuel],
Biasotti, S.[Silvia],
Hancock, E.R.[Edwin R.],
Shape Simplification Through Graph Sparsification,
GbRPR17(13-22).
Springer DOI
1706
BibRef
Fiorucci, M.[Marco],
Torcinovich, A.[Alessandro],
Curado, M.[Manuel],
Escolano, F.[Francisco],
Pelillo, M.[Marcello],
On the Interplay Between Strong Regularity and Graph Densification,
GbRPR17(165-174).
Springer DOI
1706
BibRef
Curado, M.[Manuel],
Escolano, F.[Francisco],
Lozano, M.A.[Miguel Angel],
Hancock, E.R.[Edwin R.],
Dirichlet densifiers for improved commute times estimation,
PR(91), 2019, pp. 56-68.
Elsevier DOI
1904
BibRef
Earlier:
Dirichlet Densifiers: Beyond Constraining the Spectral Gap,
SSSPR18(512-521).
Springer DOI
1810
BibRef
Earlier: A2, A1, A3, A4:
Dirichlet Graph Densifiers,
SSSPR16(185-195).
Springer DOI
1611
BibRef
Curado, M.[Manuel],
Lozano, M.A.[Miguel A.],
Escolano, F.[Francisco],
Hancock, E.R.[Edwin R.],
Dirichlet densifier bounds: Densifying beyond the spectral gap
constraint,
PRL(125), 2019, pp. 425-431.
Elsevier DOI
1909
Graph densification, Commute times, Spectral graph theory
BibRef
Escolano, F.[Francisco],
Curado, M.[Manuel],
Hancock, E.R.[Edwin R.],
Commute Times in Dense Graphs,
SSSPR16(241-251).
Springer DOI
1611
BibRef
Escolano, F.[Francisco],
Lozano, M.A.[Miguel A.],
Hancock, E.R.[Edwin R.],
Heat Flow-Thermodynamic Depth Complexity in Networks,
ICPR10(1578-1581).
IEEE DOI
1008
BibRef
Earlier: A1, A3, A2:
Birkhoff polytopes, heat kernels and graph complexity,
ICPR08(1-5).
IEEE DOI
0812
BibRef
Kokiopoulou, E.[Effrosyni],
Frossard, P.[Pascal],
Minimum Distance between Pattern Transformation Manifolds:
Algorithm and Applications,
PAMI(31), No. 7, July 2009, pp. 1225-1238.
IEEE DOI
0905
Transformation invariance.
Minimum distance between the transformation manifolds spanned by
patterns of interest.
BibRef
Kokiopoulou, E.[Effrosyni],
Kressner, D.,
Frossard, P.[Pascal],
Optimal Image Alignment With Random Projections of Manifolds:
Algorithm and Geometric Analysis,
IP(20), No. 6, June 2011, pp. 1543-1557.
IEEE DOI
1106
BibRef
Kokiopoulou, E.[Effrosyni],
Frossard, P.[Pascal],
Graph-based classification of multiple observation sets,
PR(43), No. 12, December 2010, pp. 3988-3997.
Elsevier DOI
1003
BibRef
And:
Distributed classification of multiple observations by consensus,
ICIP10(2697-2700).
IEEE DOI
1009
Graph-based classification; Multiple observations sets; Video face
recognition; Multi-view object recognition
BibRef
Kokiopoulou, E.[Effrosyni],
Pirillos, S.[Stefanos],
Frossard, P.[Pascal],
Graph-based classification for multiple observations of transformed
patterns,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Frossard, P.[Pascal],
Khasanova, R.,
Graph-Based Classification of Omnidirectional Images,
DeepLearn-G17(860-869)
IEEE DOI
1802
Cameras, Geometry, Lenses, Machine learning, Robot vision systems
BibRef
Lezoray, O.[Olivier],
Ta, V.T.[Vinh-Thong],
El Moataz, A.[Abderrahim],
Partial differences as tools for filtering data on graphs,
PRL(31), No. 14, 15 October 2010, pp. 2201-2213.
Elsevier DOI
1003
Partial difference equations; Weighted graphs; Mathematical
morphology; Anisotropic and isotropic discrete regularization
BibRef
Leordeanu, M.[Marius],
Sukthankar, R.[Rahul],
Hebert, M.[Martial],
Unsupervised Learning for Graph Matching,
IJCV(96), No. 1, January 2012, pp. 28-45.
WWW Link.
1201
BibRef
Leordeanu, M.[Marius],
Hebert, M.[Martial],
Unsupervised learning for graph matching,
CVPR09(864-871).
IEEE DOI
0906
BibRef
Leordeanu, M.[Marius],
Spectral Graph Matching, Learning, and Inference for Computer Vision,
CMU-RI-TR-09-27, July, 2009.
BibRef
0907
Ph.D.Thesis, Carnegie Mellon University, July, 2009.
WWW Link.
1102
BibRef
Leordeanu, M.[Marius],
Hebert, M.[Martial],
A Spectral Technique for Correspondence Problems Using Pairwise
Constraints,
ICCV05(II: 1482-1489).
IEEE DOI
0510
BibRef
Leordeanu, M.[Marius],
Hebert, M.[Martial],
Pairwise Grouping Using Color,
CMU-RI-TR-08-46, December, 2008.
WWW Link.
BibRef
0812
Leordeanu, M.[Marius],
Hebert, M.[Martial],
Sukthankar, R.[Rahul],
Beyond Local Appearance: Category Recognition from Pairwise
Interactions of Simple Features,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Leordeanu, M.[Marius],
Zanfir, A.[Andrei],
Sminchisescu, C.[Cristian],
Locally Affine Sparse-to-Dense Matching for Motion and Occlusion
Estimation,
ICCV13(1721-1728)
IEEE DOI
1403
Feature Matching
BibRef
Leordeanu, M.[Marius],
Zanfir, A.[Andrei],
Sminchisescu, C.[Cristian],
Semi-supervised learning and optimization for hypergraph matching,
ICCV11(2274-2281).
IEEE DOI
1201
BibRef
Tang, J.[Jin],
Jiang, B.[Bo],
Zheng, A.[Aihua],
Luo, B.[Bin],
Graph matching based on spectral embedding with missing value,
PR(45), No. 10, October 2012, pp. 3768-3779.
Elsevier DOI
1206
Dot product representation of graph; Missing value; Association graph;
Co-embedding; Point pattern matching
BibRef
Jiang, B.[Bo],
Zhao, H.F.[Hai-Feng],
Tang, J.[Jin],
Luo, B.[Bin],
A sparse nonnegative matrix factorization technique for graph
matching problems,
PR(47), No. 2, 2014, pp. 736-747.
Elsevier DOI
1311
Graph matching
BibRef
Jiang, B.[Bo],
Tang, J.[Jin],
Cao, X.C.[Xiao-Chun],
Luo, B.[Bin],
Lagrangian relaxation graph matching,
PR(61), No. 1, 2017, pp. 255-265.
Elsevier DOI
1609
Graph matching
BibRef
Jiang, B.[Bo],
Tang, J.[Jin],
Zheng, A.[Aihua],
Luo, B.[Bin],
Image representation and matching with geometric-edge random
structure graph,
PRL(87), No. 1, 2017, pp. 20-28.
Elsevier DOI
1703
Image representation
BibRef
Jiang, B.[Bo],
Tang, J.[Jin],
Luo, B.[Bin],
Efficient Feature Matching via Nonnegative Orthogonal Relaxation,
IJCV(127), No. 9, September 2019, pp. 1345-1360.
Springer DOI
1908
BibRef
Jiang, B.[Bo],
Tang, J.[Jin],
Ding, C.,
Luo, B.[Bin],
Binary Constraint Preserving Graph Matching,
CVPR17(550-557)
IEEE DOI
1711
BibRef
Earlier: A1, A2, A4, Only:
Attributed Relational Graph Matching with Sparse Relaxation and
Bistochastic Normalization,
GbRPR15(218-227).
Springer DOI
1511
BibRef
Earlier: A2, A1, A4, Only:
Graph Matching Based on Dot Product Representation of Graphs,
GbRPR11(175-184).
Springer DOI
1105
Computational modeling, Convergence,
Projection algorithms, Quadratic programming.
BibRef
Prakash, S.[Surya],
Robles-Kelly, A.[Antonio],
Geometric graph comparison from an alignment viewpoint,
PR(45), No. 10, October 2012, pp. 3780-3794.
Elsevier DOI
1206
Graph comparison and retrieval; Graph algorithms; Graph theory
BibRef
Takaoka, A.[Asahi],
Tayu, S.[Satoshi],
Ueno, S.[Shuichi],
On Minimum Feedback Vertex Sets in Bipartite Graphs and
Degree-Constraint Graphs,
IEICE(E96-D), No. 11, November 2013, pp. 2327-2332.
WWW Link.
1311
BibRef
Wang, J.M.,
Chen, S.W.,
Fuh, C.S.,
Attributed hypergraph matching on a Riemannian manifold,
MVA(25), No. 4, May 2014, pp. 823-844.
WWW Link.
1404
BibRef
Lagraa, S.[Sofiane],
Seba, H.[Hamida],
Khennoufa, R.[Riadh],
M'Baya, A.[Abir],
Kheddouci, H.[Hamamache],
A distance measure for large graphs based on prime graphs,
PR(47), No. 9, 2014, pp. 2993-3005.
Elsevier DOI
1406
Graph similarity
BibRef
Leng, C.C.[Cheng-Cai],
Xu, W.[Wei],
Cheng, I.,
Basu, A.,
Graph Matching Based on Stochastic Perturbation,
IP(24), No. 12, December 2015, pp. 4862-4875.
IEEE DOI
1512
eigenvalues and eigenfunctions
BibRef
Vogelstein, J.T.,
Roncal, W.G.,
Vogelstein, R.J.,
Priebe, C.E.,
Graph Classification Using Signal-Subgraphs:
Applications in Statistical Connectomics,
PAMI(35), No. 7, 2013, pp. 1539-1551.
IEEE DOI medical signal processing; graph classification; Brain modeling
1307
BibRef
Lyzinski, V.,
Fishkind, D.E.,
Fiori, M.,
Vogelstein, J.T.,
Priebe, C.E.,
Sapiro, G.,
Graph Matching: Relax at Your Own Risk,
PAMI(38), No. 1, January 2016, pp. 60-73.
IEEE DOI
1601
Bismuth.
BibRef
Sussman, D.L.,
Park, Y.,
Priebe, C.E.,
Lyzinski, V.,
Matched Filters for Noisy Induced Subgraph Detection,
PAMI(42), No. 11, November 2020, pp. 2887-2900.
IEEE DOI
2010
Noise measurement, Approximation algorithms, Correlation,
Social networking (online),
graph matching
BibRef
Chen, L.,
Shen, C.C.[Cen-Cheng],
Vogelstein, J.T.[Joshua T.],
Priebe, C.E.[Carey E.],
Robust Vertex Classification,
PAMI(38), No. 3, March 2016, pp. 578-590.
IEEE DOI
1602
Analytical models
BibRef
Zhang, H.Y.[Heng-Yuan],
Chen, X.W.[Xiao-Wu],
Li, J.[Jia],
Zhou, B.[Bin],
Fuzzy community detection via modularity guided membership-degree
propagation,
PRL(70), No. 1, 2016, pp. 66-72.
Elsevier DOI
1602
Fuzzy community detection
BibRef
Savage, N.[Neil],
Graph Matching in Theory and Practice,
CACM(59), No. 7, July 2016, pp. 12-14.
DOI Link
1608
New algorithm for graph isomorphism.
See also Graph Isomorphism in Quasipolynomial Time.
BibRef
Wang, T.[Tao],
Ling, H.B.[Hai-Bin],
Lang, C.Y.[Cong-Yan],
Feng, S.H.[Song-He],
Symmetry-aware graph matching,
PR(60), No. 1, 2016, pp. 657-668.
Elsevier DOI
1609
Symmetry
BibRef
Chen, R.[Ran],
Lang, C.Y.[Cong-Yan],
Wang, T.[Tao],
Multiple path exploration for graph matching,
MVA(28), No. 7, October 2017, pp. 695-703.
Springer DOI
1710
singular point discovering by checking the smoothness of the path.
BibRef
Park, H.M.[Han-Mu],
Yoon, K.J.[Kuk-Jin],
Encouraging second-order consistency for multiple graph matching,
MVA(27), No. 7, October 2016, pp. 1021-1034.
Springer DOI
1610
BibRef
Abu-Aisheh, Z.[Zeina],
Anytime and Distributed Approaches for Graph Matching,
ELCVIA(15), No. 2, 2016, pp. 13-15.
DOI Link
1611
BibRef
Abu-Aisheh, Z.[Zeina],
Raveaux, R.[Romain],
Ramel, J.Y.[Jean-Yves],
Anytime graph matching,
PRL(84), No. 1, 2016, pp. 215-224.
Elsevier DOI
1612
BibRef
Earlier:
A Graph Database Repository and Performance Evaluation Metrics for
Graph Edit Distance,
GbRPR15(138-147).
Springer DOI
1511
Graph matching
BibRef
Zhang, H.[He],
Ren, P.[Peng],
Game theoretic hypergraph matching for multi-source image
correspondences,
PRL(87), No. 1, 2017, pp. 87-95.
Elsevier DOI
1703
Hypergraph matching
BibRef
Zhang, H.[He],
Du, B.[Bin],
Wang, Y.J.[Yan-Jiang],
Ren, P.[Peng],
A Hypergraph Matching Framework for Refining Multi-source Feature
Correspondences,
GbRPR15(108-117).
Springer DOI
1511
BibRef
Wang, Z.S.[Zhen-Sheng],
Yue, Y.[Yang],
Li, Q.Q.[Qing-Quan],
Nie, K.[Ke],
Yu, C.B.[Chang-Bin],
Analysis of the Spatial Variation of Network-Constrained Phenomena
Represented by a Link Attribute Using a Hierarchical Bayesian Model,
IJGI(6), No. 2, 2017, pp. xx-yy.
DOI Link
1703
BibRef
Nguyen, Q.[Quynh],
Tudisco, F.[Francesco],
Gautier, A.[Antoine],
Hein, M.[Matthias],
An Efficient Multilinear Optimization Framework for Hypergraph
Matching,
PAMI(39), No. 6, June 2017, pp. 1054-1075.
IEEE DOI
1705
Algorithm design and analysis, Approximation algorithms,
Optimization, Pattern matching, Tensile stress,
Hypergraph Matching,
block coordinate ascent, multilinear form, tensor
BibRef
Ngoc, Q.N.[Quynh Nguyen],
Gautier, A.[Antoine],
Hein, M.[Matthias],
A flexible tensor block coordinate ascent scheme for hypergraph
matching,
CVPR15(5270-5278)
IEEE DOI
1510
BibRef
Zhuang, L.S.[Lian-Sheng],
Zhou, Z.H.[Zi-Han],
Gao, S.H.[Sheng-Hua],
Yin, J.W.[Jing-Wen],
Lin, Z.C.[Zhou-Chen],
Ma, Y.[Yi],
Label Information Guided Graph Construction for Semi-Supervised
Learning,
IP(26), No. 9, September 2017, pp. 4182-4192.
IEEE DOI
1708
convex programming, graph theory, knowledge representation,
learning (artificial intelligence),
convex optimization problem,
label information guided graph construction, label propagation,
linearized alternating direction method,
BibRef
Stankovic, L.[Ljubiša],
Sejdic, E.[Ervin],
Dakovic, M.[Miloš],
Vertex-Frequency Energy Distributions,
SPLetters(25), No. 3, March 2018, pp. 358-362.
IEEE DOI
1802
Artificial neural networks, Indexes, Laplace equations,
Matrix decomposition, Signal processing, Smoothing methods,
vertex-frequency
BibRef
Stankovic, L.[Ljubiša],
Sejdic, E.[Ervin],
Dakovic, M.[Miloš],
Reduced Interference Vertex-Frequency Distributions,
SPLetters(25), No. 9, September 2018, pp. 1393-1397.
IEEE DOI
1809
approximation theory, Fourier transforms, graph theory,
signal representation, wavelet transforms, Wigner distribution,
vertex-frequency analysis
BibRef
Kim, S.[Saehoon],
Choi, S.J.[Seung-Jin],
Sparse Circulant Binary Embedding: An Asymptotic Analysis,
SPLetters(25), No. 3, March 2018, pp. 432-436.
IEEE DOI
1802
Binary codes, Convergence, Hamming distance, Quantization (signal),
Sparse matrices, Time complexity, Binary embedding (BE),
sparse embedding
BibRef
Isufi, E.[Elvin],
Mahabir, A.S.U.[Ashvant S.U.],
Leus, G.[Geert],
Blind Graph Topology Change Detection,
SPLetters(25), No. 5, May 2018, pp. 655-659.
IEEE DOI
1805
Fourier transforms, graph theory, topology,
blind graph topology change detection,
matched subspace detection
BibRef
Giannakis, G.B.,
Shen, Y.,
Karanikolas, G.V.,
Topology Identification and Learning over Graphs:
Accounting for Nonlinearities and Dynamics,
PIEEE(106), No. 5, May 2018, pp. 787-807.
IEEE DOI
1805
Brain modeling, Dimensionality reduction, Graph theory,
Network topology, Principal component analysis,
time-varying networks
BibRef
Cadena, J.,
Chen, F.,
Vullikanti, A.,
Graph Anomaly Detection Based on Steiner Connectivity and Density,
PIEEE(106), No. 5, May 2018, pp. 829-845.
IEEE DOI
1805
Anomaly detection, Approximation algorithms, Computer science,
Graph theory, Graphical models, Sensors,
scan statistics
BibRef
De, J.[Jaydeep],
Zhang, X.W.[Xiao-Wei],
Lin, F.[Feng],
Cheng, L.[Li],
Transduction on Directed Graphs via Absorbing Random Walks,
PAMI(40), No. 7, July 2018, pp. 1770-1784.
IEEE DOI
1806
Algorithm design and analysis, Bidirectional control, Kernel,
Laplace equations, Markov processes, Prediction algorithms,
transductive learning
BibRef
Ciesielski, K.C.[Krzysztof Chris],
Falcão, A.X.[Alexandre Xavier],
Miranda, P.A.V.[Paulo A. V.],
Path-Value Functions for Which Dijkstra's Algorithm Returns Optimal
Mapping,
JMIV(60), No. 7, September 2018, pp. 1025-1036.
Springer DOI
1808
Dijkstra graph search.
BibRef
Liu, Y.[Yike],
Safavi, T.[Tara],
Dighe, A.[Abhilash],
Koutra, D.[Danai],
Graph Summarization Methods and Applications: A Survey,
Surveys(51), No. 3, July 2018, pp. Article No 62.
DOI Link
1809
Dealing with enormous amounts of data.
BibRef
Sabetghadam, S.[Serwah],
Lupu, M.[Mihai],
Bierig, R.[Ralf],
Rauber, A.[Andreas],
A faceted approach to reachability analysis of graph modelled
collections,
MultInfoRetr(8), No. 3, September 2018, pp. 157-171.
Springer DOI
1809
BibRef
Wang, T.[Tao],
Ling, H.B.[Hai-Bin],
Lang, C.Y.[Cong-Yan],
Feng, S.,
Graph Matching with Adaptive and Branching Path Following,
PAMI(40), No. 12, December 2018, pp. 2853-2867.
IEEE DOI
1811
Band-pass filters, Algorithm design and analysis,
Probabilistic logic, Approximation algorithms, Pattern matching,
adaptive path estimation
BibRef
Wang, T.[Tao],
Ling, H.B.[Hai-Bin],
Lang, C.Y.[Cong-Yan],
Wu, J.[Jun],
Branching Path Following for Graph Matching,
ECCV16(II: 508-523).
Springer DOI
1611
BibRef
Zhang, R.,
Wang, W.,
Second- and High-Order Graph Matching for Correspondence Problems,
CirSysVideo(28), No. 10, October 2018, pp. 2978-2992.
IEEE DOI
1811
Robustness, Tensile stress, Optimization, Feature extraction,
Search problems, Probabilistic logic, Complexity theory,
Markov chain Monte Carlo
BibRef
Griffith, D.A.[Daniel A.],
Generating random connected planar graphs,
GeoInfo(22), No. 4, October 2018, pp. 767-782.
Springer DOI
1811
E.g. for testing purposes. Generate an appropriate sample graph.
BibRef
Fishkind, D.E.[Donniell E.],
Adali, S.[Sancar],
Patsolic, H.G.[Heather G.],
Meng, L.Y.[Ling-Yao],
Singh, D.[Digvijay],
Lyzinski, V.[Vince],
Priebe, C.E.[Carey E.],
Seeded graph matching,
PR(87), 2019, pp. 203-215.
Elsevier DOI
1812
Hungarian algorithm, Quadratic assignment problem (QAP), Vertex alignment
BibRef
Zhou, J.[Jun],
Wang, T.[Tao],
Lang, C.Y.[Cong-Yan],
Feng, S.H.[Song-He],
Jin, Y.[Yi],
A novel hypergraph matching algorithm based on tensor refining,
JVCIR(57), 2018, pp. 69-75.
Elsevier DOI
1812
Hypergraph matching, Probabilistic, Tensor refining
BibRef
Allili, M.[Madjid],
Kaczynski, T.[Tomasz],
Landi, C.[Claudia],
Masoni, F.[Filippo],
Acyclic Partial Matchings for Multidimensional Persistence: Algorithm
and Combinatorial Interpretation,
JMIV(61), No. 2, February 2019, pp. 174-192.
Springer DOI
1902
BibRef
Earlier:
Algorithmic Construction of Acyclic Partial Matchings for
Multidimensional Persistence,
DGCI17(375-387).
Springer DOI
1711
BibRef
Lee, C.,
Lee, H.,
Effective Parallelization of a High-Order Graph Matching Algorithm
for GPU Execution,
CirSysVideo(29), No. 2, February 2019, pp. 560-571.
IEEE DOI
1902
Graphics processing units, Approximation algorithms,
Feature extraction, Signal processing algorithms,
parallel processing
BibRef
Nawaz, M.[Mehmood],
Khan, S.[Sheheryar],
Qureshi, R.[Rizwan],
Yan, H.[Hong],
Clustering based one-to-one hypergraph matching with a large number
of feature points,
SP:IC(74), 2019, pp. 289-298.
Elsevier DOI
1904
Cluster matching, Tensor matching, Geometric deformation, Sub-hypergraphs
BibRef
Yang, J.[Jing],
Yang, X.[Xu],
Zhou, Z.B.[Zhang-Bing],
Liu, Z.Y.[Zhi-Yong],
Sub-hypergraph matching based on adjacency tensor,
CVIU(183), 2019, pp. 1-10.
Elsevier DOI
1906
Hypergraph matching, Subgraph matching, High order structure, Adjacency tensor
BibRef
Cai, Z.[Zhuang],
Zhang, K.[Kang],
Hu, D.N.[Dong-Ni],
Visualizing large graphs by layering and bundling graph edges,
VC(35), No. 5, May 2019, pp. 739-751.
Springer DOI
1906
Edge bundling for graphs.
BibRef
Nie, W.,
Liu, A.,
Gao, Y.,
Su, Y.,
Hyper-Clique Graph Matching and Applications,
CirSysVideo(29), No. 6, June 2019, pp. 1619-1630.
IEEE DOI
1906
Linear programming, Tensile stress, Task analysis,
Biomedical measurement, Robustness, Solid modeling,
multi-view object retrieval
BibRef
Wang, S.H.[Shu-Hui],
Li, L.[Liang],
Yang, C.X.[Chen-Xue],
Huang, Q.M.[Qing-Ming],
Regularized topic-aware latent influence propagation in dynamic
relational networks,
GeoInfo(23), No. 3, July 2019, pp. 329-352.
WWW Link.
1908
BibRef
Arrigoni, F.[Federica],
Fusiello, A.[Andrea],
Bearing-Based Network Localizability: A Unifying View,
PAMI(41), No. 9, Sep. 2019, pp. 2049-2069.
IEEE DOI
1908
Problem of establishing whether a set of directions between pairs of
nodes uniquely determines (up to translation and scale) the position
of the nodes in d-space.
Structure from motion, Indexes, Q measurement, Cameras,
Noise measurement, Computational modeling, Position measurement.
BibRef
Fiorucci, M.[Marco],
Pelosin, F.[Francesco],
Pelillo, M.[Marcello],
Separating Structure from Noise in Large Graphs Using the Regularity
Lemma,
PR(98), 2020, pp. 107070.
Elsevier DOI
1911
Regularity lemma, Graph summarization, Structural patterns,
Noise, Randomness, Graph similarity search
BibRef
Zheng, Y.[Yali],
Pan, L.[Lili],
Qian, J.[Jiye],
Guo, H.L.[Hong-Liang],
Fast matching via ergodic markov chain for super-large graphs,
PR(106), 2020, pp. 107418.
Elsevier DOI
2006
Spectral matching, Graph matching, Ergodic markov chain, Space complexity
BibRef
Malmberg, F.[Filip],
Ciesielski, K.C.[Krzysztof Chris],
Two Polynomial Time Graph Labeling Algorithms Optimizing Max-Norm-Based
Objective Functions,
JMIV(62), No. 5, June 2020, pp. 737-750.
Springer DOI
2007
BibRef
Wang, F.D.[Fu-Dong],
Xue, N.[Nan],
Yu, J.,
Xia, G.S.[Gui-Song],
Zero-Assignment Constraint for Graph Matching With Outliers,
CVPR20(3030-3039)
IEEE DOI
2008
Artificial intelligence, Linear programming,
Optimization, Time complexity, Indexes
BibRef
Wang, F.D.[Fu-Dong],
Xue, N.[Nan],
Zhang, Y.P.[Yi-Peng],
Bai, X.[Xiang],
Xia, G.S.[Gui-Song],
Adaptively Transforming Graph Matching,
ECCV18(XVI: 646-662).
Springer DOI
1810
BibRef
Wong, W.K.[Wai Keung],
Han, N.[Na],
Fang, X.Z.[Xiao-Zhao],
Zhan, S.H.[Shan-Hua],
Wen, J.[Jie],
Clustering Structure-Induced Robust Multi-View Graph Recovery,
CirSysVideo(30), No. 10, October 2020, pp. 3584-3597.
IEEE DOI
2010
Sparse matrices, Learning systems, Optimization, Task analysis,
Laplace equations, Clustering algorithms, Noise measurement,
alternating optimization
BibRef
Yu, Y.F.,
Xu, G.,
Jiang, M.,
Zhu, H.,
Dai, D.Q.,
Yan, H.,
Joint Transformation Learning via the L2,1-Norm Metric for Robust
Graph Matching,
Cyber(51), No. 2, February 2021, pp. 521-533.
IEEE DOI
2101
Measurement, Deformable models, Strain, Linear programming,
Robustness, Task analysis, Graph matching,
similarity metric
BibRef
Zeng, S.F.[Shao-Feng],
Liu, Z.Y.[Zhi-Yong],
Yang, X.[Xu],
Supervised learning for parameterized Koopmans-Beckmann's graph
matching,
PRL(143), 2021, pp. 8-13.
Elsevier DOI
2102
Graph matching, Koopmans-Beckmann, Supervised learning, Structured SVM
BibRef
Bouhenni, S.[Sarra],
Yahiaoui, S.[Said],
Nouali-Taboudjemat, N.[Nadia],
Kheddouci, H.[Hamamache],
A Survey on Distributed Graph Pattern Matching in Massive Graphs,
Surveys(54), No. 2, February 2021, pp. xx-yy.
DOI Link
2104
Survey, Graph Matching. graph simulation, subgraph isomorphism, distributed graphs,
Graph pattern matching
BibRef
Aziz, F.[Furqan],
Akbar, M.S.[Mian Saeed],
Jawad, M.[Muhammad],
Malik, A.H.[Abdul Haseeb],
Uddin, M.I.[M. Irfan],
Gkoutos, G.V.[Georgios V.],
Graph characterisation using graphlet-based entropies,
PRL(147), 2021, pp. 100-107.
Elsevier DOI
2106
Graph entropy, Graph characterisation, Information functional, Graphlets
BibRef
Xu, Y.[Yan],
Feng, Z.D.[Zhi-Dan],
Qi, X.Q.[Xing-Qin],
Signless-Laplacian Eigenvector Centrality: A Novel Vital Nodes
Identification Method for Complex Networks,
PRL(148), 2021, pp. 7-14.
Elsevier DOI
2107
Centrality, Signless-laplacian matrix, Graph theory, Primitive matrix
BibRef
Conte, D.[Donatello],
Grossi, G.[Giuliano],
Lanzarotti, R.[Raffaella],
Lin, J.Y.[Jian-Yi],
Petrini, A.[Alessandro],
Analysis of a parallel MCMC algorithm for graph coloring with nearly
uniform balancing,
PRL(149), 2021, pp. 30-36.
Elsevier DOI
2108
Graph coloring, Markov chain Monte Carlo method,
Color balancing, Parallel algorithms
BibRef
Jiang, Z.T.[Ze-Tian],
Wang, T.Z.[Tian-Zhe],
Yan, J.C.[Jun-Chi],
Unifying Offline and Online Multi-Graph Matching via Finding Shortest
Paths on Supergraph,
PAMI(43), No. 10, October 2021, pp. 3648-3663.
IEEE DOI
2109
Pattern matching, Heuristic algorithms, Dynamic programming,
Optimization, Shortest path problem, Computational modeling,
shortest path search
BibRef
Saboksayr, S.S.[Seyed Saman],
Mateos, G.[Gonzalo],
Accelerated Graph Learning From Smooth Signals,
SPLetters(28), 2021, pp. 2192-2196.
IEEE DOI
2112
Signal processing algorithms, Convergence, Topology,
Network topology, Inference algorithms, Convex functions, Tuning,
topology identification
BibRef
Kiouche, A.E.[Abd Errahmane],
Seba, H.[Hamida],
Amrouche, K.[Karima],
A maximum diversity-based path sparsification for geometric graph
matching,
PRL(152), 2021, pp. 107-114.
Elsevier DOI
2112
Geometric graphs, Shape matching, Graph matching,
Graph sparsification, Maximum diversity problem
BibRef
Gui, S.P.[Shu-Peng],
Zhang, X.L.[Xiang-Liang],
Zhong, P.[Pan],
Qiu, S.[Shuang],
Wu, M.R.[Ming-Rui],
Ye, J.P.[Jie-Ping],
Wang, Z.D.[Zheng-Dao],
Liu, J.[Ji],
PINE: Universal Deep Embedding for Graph Nodes via Partial
Permutation Invariant Set Functions,
PAMI(44), No. 2, February 2022, pp. 770-782.
IEEE DOI
2201
Vector representation of nodes in a graph.
Task analysis, Laplace equations, Aggregates,
Reinforcement learning, Matrix decomposition,
representation learning
BibRef
Cai, L.[Lei],
Li, J.D.[Jun-Dong],
Wang, J.[Jie],
Ji, S.W.[Shui-Wang],
Line Graph Neural Networks for Link Prediction,
PAMI(44), No. 9, September 2022, pp. 5103-5113.
IEEE DOI
2208
Feature extraction, Task analysis, Predictive models,
Graph neural networks, Convolution, Deep learning, Topology,
line graphs
BibRef
Meng, X.H.[Xiang-Hu],
Li, J.[Jun],
Zhou, M.C.[Meng-Chu],
Dai, X.Z.[Xian-Zhong],
A Dynamic Colored Traveling Salesman Problem With Varying Edge
Weights,
ITS(23), No. 8, August 2022, pp. 13549-13558.
IEEE DOI
2208
Urban areas, Vehicle dynamics, Logistics, Color, Optimization, Costs,
Statistics, Dynamic colored traveling salesman problem,
dynamic optimization problem
BibRef
Xu, H.[Hao],
Sang, S.Q.[Sheng-Qi],
Bai, P.Z.[Pei-Zhen],
Li, R.[Ruike],
Yang, L.[Laurence],
Lu, H.P.[Hai-Ping],
GripNet: Graph information propagation on supergraph for
heterogeneous graphs,
PR(133), 2023, pp. 108973.
Elsevier DOI
2210
Graph representation learning, Heterogeneous graph,
Data integration, Multi-relational link prediction, Node classification
BibRef
Yu, Y.F.[Yu-Feng],
Chen, L.[Long],
Huang, K.K.[Ke-Kun],
Zhu, H.[Hu],
Xu, G.X.[Guo-Xia],
Kernel embedding transformation learning for graph matching,
PRL(163), 2022, pp. 136-144.
Elsevier DOI
2212
Transformation learning, Graph matching, Deformation variation,
Correspondence
BibRef
Wu, H.[Hanrui],
Yan, Y.G.[Yu-Guang],
Ng, M.K.P.[Michael Kwok-Po],
Hypergraph Collaborative Network on Vertices and Hyperedges,
PAMI(45), No. 3, March 2023, pp. 3245-3258.
IEEE DOI
2302
Standards, Correlation, Convolution, Collaborative work, Task analysis,
Data models, Training, Edge classification, vertex classification
BibRef
Li, J.[Jia],
Huang, Y.F.[Yong-Feng],
Chang, H.[Heng],
Rong, Y.[Yu],
Semi-Supervised Hierarchical Graph Classification,
PAMI(45), No. 5, May 2023, pp. 6265-6276.
IEEE DOI
2304
Social networking (online), Mutual information, Training,
Task analysis, Proteins, Data models, Computational modeling,
semi-supervised learning
BibRef
Zhu, L.L.[Liang-Liang],
Zhu, X.W.[Xin-Wen],
Geng, X.R.[Xiu-Rui],
Factorized multi-Graph matching,
PR(140), 2023, pp. 109597.
Elsevier DOI
2305
Graph matching, Multi-graph matching, Tensor, Factorization
BibRef
Shen, C.C.[Cen-Cheng],
Wang, Q.Z.[Qi-Zhe],
Priebe, C.E.[Carey E.],
One-Hot Graph Encoder Embedding,
PAMI(45), No. 6, June 2023, pp. 7933-7938.
IEEE DOI
2305
Laplace equations, Standards, Matlab, Training data, Testing,
Stochastic processes, Sparse matrices, Central limit theorem,
vertex classification
BibRef
Hirchoua, B.[Badr],
El Motaki, S.[Saloua],
ß-Random Walk: Collaborative sampling and weighting mechanisms based
on a single parameter for node embeddings,
PR(142), 2023, pp. 109730.
Elsevier DOI
2307
Graph embedding transforms a graph into vector representations.
Node embedding, Random walk, Knowledge representation,
Link prediction, Knowledge completion, Node behavior
BibRef
Du, H.Y.[Hang-Yuan],
Wang, W.J.[Wen-Jian],
Bai, L.[Liang],
Dual-channel embedding learning model for partially labeled
attributed networks,
PR(142), 2023, pp. 109644.
Elsevier DOI
2307
Convert a input network into a low-dimensional space.
Partially labeled attributed networks, Network embedding,
Mutual information, Graph convolution networks, Information redundancy
BibRef
Wang, R.Z.[Run-Zhong],
Yan, J.C.[Jun-Chi],
Yang, X.K.[Xiao-Kang],
Unsupervised Learning of Graph Matching With Mixture of Modes via
Discrepancy Minimization,
PAMI(45), No. 8, August 2023, pp. 10500-10518.
IEEE DOI
2307
Unsupervised learning, Pipelines, Image matching,
Benchmark testing, Visualization, Computational modeling,
unsupervised learning
BibRef
Xie, T.[Tian],
Kannan, R.[Rajgopal],
Kuo, C.C.J.[C.C. Jay],
Label Efficient Regularization and Propagation for Graph Node
Classification,
PAMI(45), No. 12, December 2023, pp. 14856-14871.
IEEE DOI
2311
BibRef
Park, J.D.[Jin-Duk],
Tran, C.[Cong],
Shin, W.Y.[Won-Yong],
Cao, X.[Xin],
On the Power of Gradual Network Alignment Using Dual-Perception
Similarities,
PAMI(45), No. 12, December 2023, pp. 15292-15307.
IEEE DOI
2311
BibRef
Wu, H.[Hanrui],
Li, N.[Nuosi],
Zhang, J.[Jia],
Chen, S.[Sentao],
Ng, M.K.[Michael K.],
Long, J.Y.[Jin-Yi],
Collaborative contrastive learning for hypergraph node classification,
PR(146), 2024, pp. 109995.
Elsevier DOI
2311
Hypergraph, Hypergraph convolution, Contrastive learning,
Graph convolution, Node classification
BibRef
Cui, L.X.[Li-Xin],
Li, M.[Ming],
Bai, L.[Lu],
Wang, Y.[Yue],
Li, J.[Jing],
Wang, Y.C.[Yan-Chao],
Li, Z.[Zhao],
Chen, Y.[Yunwen],
Hancock, E.R.[Edwin R.],
QBER: Quantum-based Entropic Representations for un-attributed graphs,
PR(145), 2024, pp. 109877.
Elsevier DOI
2311
Graph embedding, Graph entropy, Quantum walks, Entropic representations
BibRef
Ren, Z.Q.[Zhong-Qiang],
Rubinstein, Z.B.[Zachary B.],
Smith, S.F.[Stephen F.],
Rathinam, S.[Sivakumar],
Choset, H.[Howie],
ERCA*: A New Approach for the Resource Constrained Shortest Path
Problem,
ITS(24), No. 12, December 2023, pp. 14994-15005.
IEEE DOI
2312
BibRef
Feng, Y.F.[Yi-Fan],
Han, J.[Jiashu],
Ying, S.H.[Shi-Hui],
Gao, Y.[Yue],
Hypergraph Isomorphism Computation,
PAMI(46), No. 5, May 2024, pp. 3880-3896.
IEEE DOI
2404
Kernel, Correlation, Computational efficiency, Color, Runtime,
Proteins, Memory management, High-Order correlation, hypergraph,
hypergraph isomorphism
BibRef
He, J.W.[Jia-Wei],
Huang, Z.[Zehao],
Wang, N.[Naiyan],
Zhang, Z.X.[Zhao-Xiang],
Learnable Graph Matching: A Practical Paradigm for Data Association,
PAMI(46), No. 7, July 2024, pp. 4880-4895.
IEEE DOI
2406
Task analysis, Image matching, Feature extraction, Point cloud compression,
Optimization, Image edge detection, image matching
BibRef
Alcayde, A.[Alfredo],
Ventura, J.[Jorge],
Montoya, F.G.[Francisco G.],
Hypercomplex Techniques in Signal and Image Processing Using Network
Graph Theory: Identifying core research directions,
SPMag(41), No. 2, March 2024, pp. 14-28.
IEEE DOI
2406
[Hypercomplex Signal and Image Processing]
Navigation, Algebra, Image processing, Quaternions,
Research initiatives, Complexity theory, Metadata,
Graph theory
BibRef
Deng, Z.D.[Zhi-Dong],
Wang, J.Y.[Jing-Yi],
G2-SCANN: Gaussian-kernel graph-based SLD clustering algorithm with
natural neighbourhood,
PR(155), 2024, pp. 110682.
Elsevier DOI
2408
Tree-like clustering algorithm, Shortest path length (SPL),
Graph-based SLD, Natural neighbourhood, Clustering accuracy
BibRef
Xu, Z.[Zhoubo],
Chen, P.Q.[Pu-Qing],
Raveaux, R.[Romain],
Yang, X.[Xin],
Liu, H.D.[Hua-Dong],
Deep graph matching meets mixed-integer linear programming: Relax or
not?,
PR(155), 2024, pp. 110697.
Elsevier DOI
2408
Feature points correspondence, Graph-based representation,
Combinatorial optimization
BibRef
Tan, H.R.[Hao-Ru],
Wang, C.[Chuang],
Wu, S.T.[Si-Tong],
Zhang, X.Y.[Xu-Yao],
Yin, F.[Fei],
Liu, C.L.[Cheng-Lin],
Ensemble Quadratic Assignment Network for Graph Matching,
IJCV(132), No. 1, January 2024, pp. 3633-3655.
Springer DOI
2409
BibRef
Averty, T.,
Boudraa, A.O.,
Daré-Emzivat, D.,
A New Family of Graph Representation Matrices:
Application to Graph and Signal Classification,
SPLetters(31), 2024, pp. 2935-2939.
IEEE DOI
2411
Laplace equations, Eigenvalues and eigenfunctions, Graph theory,
Kernel, Standards, Fourier transforms, Filtering, Visualization,
spectral graph theory
BibRef
Liu, S.X.[Shi-Xuan],
Fan, C.J.[Chang-Jun],
Cheng, K.W.[Ke-Wei],
Wang, Y.F.[Yun-Fei],
Cui, P.[Peng],
Sun, Y.Z.[Yi-Zhou],
Liu, Z.[Zhong],
Inductive Meta-Path Learning for Schema-Complex Heterogeneous
Information Networks,
PAMI(46), No. 12, December 2024, pp. 10196-10209.
IEEE DOI
2411
Task analysis, Cognition, Urban areas, Semantics, Training, Sun, Fans,
Heterogeneous information networks, meta-paths discovery
BibRef
Shen, B.[Binrui],
Niu, Q.[Qiang],
Zhu, S.X.[Sheng-Xin],
Adaptive Softassign via Hadamard-Equipped Sinkhorn,
CVPR24(17638-17647)
IEEE DOI
2410
Accuracy, Sensitivity, Stability analysis, Tuning, Pattern matching,
Numerical stability, graph matching, Sinkhorn method, softassign,
assignment problem
BibRef
Zheng, Q.X.[Qi-Xuan],
Zhang, M.[Ming],
Yan, H.[Hong],
CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR
Decomposition,
CVPR24(16036-16045)
IEEE DOI
2410
Tensors, Accuracy, Approximation algorithms,
Computational efficiency, Labeling
BibRef
Ma, X.Y.[Xiao-Yan],
Liu, L.[Ling],
Yuan, W.[Wei],
Zhang, Y.X.[Yue-Xiu],
Song, L.J.[Lian-Jun],
Summary of Static Graph Embedding Algorithms,
CVIDL23(404-411)
IEEE DOI
2403
Dimensionality reduction, Deep learning,
Neural networks, Matrix decomposition.
BibRef
Ascolese, M.[Michela],
Frosini, A.[Andrea],
Characterization and Reconstruction of Hypergraphic Pattern Sequences,
IWCIA22(301-316).
Springer DOI
2301
BibRef
Liao, X.W.[Xiao-Wei],
Xu, Y.[Yong],
Ling, H.B.[Hai-Bin],
Hypergraph Neural Networks for Hypergraph Matching,
ICCV21(1246-1255)
IEEE DOI
2203
Knowledge engineering, Deep learning, Neural networks, Employment,
Benchmark testing, Classification algorithms,
Scene analysis and understanding
BibRef
Harish, A.N.[Abhinav Narayan],
Nagar, R.[Rajendra],
Raman, S.[Shanmuganathan],
RGL-NET: A Recurrent Graph Learning framework for Progressive Part
Assembly,
WACV22(647-656)
IEEE DOI
2202
Actuators, Shape, Planning, Task analysis, Collision avoidance,
Vision for Robotics
BibRef
Ehm, V.[Viktoria],
Cremers, D.[Daniel],
Bernard, F.[Florian],
Shortest Paths in Graphs with Matrix-Valued Edges:
Concepts, Algorithm and Application to 3D Multi-Shape Analysis,
3DV21(1186-1195)
IEEE DOI
2201
Shortest path problem, Visualization, Solid modeling, Shape,
Image edge detection, Computational modeling, graph algorithms,
shortest paths
BibRef
Gao, Q.K.[Quan-Kai],
Wang, F.D.[Fu-Dong],
Xue, N.[Nan],
Yu, J.G.[Jin-Gang],
Xia, G.S.[Gui-Song],
Deep Graph Matching under Quadratic Constraint,
CVPR21(5067-5074)
IEEE DOI
2111
Deep learning, Training, Codes, Feature extraction, Pattern matching
BibRef
Boll, B.[Bastian],
Schwarz, J.[Jonathan],
Schnörr, C.[Christoph],
On the Correspondence Between Replicator Dynamics and Assignment Flows,
SSVM21(373-384).
Springer DOI
2106
smooth dynamical systems for data labeling on graphs.
BibRef
Aggarwal, M.[Manasvi],
Murty, M.N.,
Region and Relations Based Multi Attention Network for Graph
Classification,
ICPR21(8101-8108)
IEEE DOI
2105
Training, Visualization, Convolution, Sensitivity analysis,
Benchmark testing, Task analysis, Datasets
BibRef
Vemavarapu, P.V.[Prabhakar V.],
Tozal, M.E.[Mehmet Engin],
Borst, C.W.[Christoph W.],
Near-Optimal Concentric Circles Layout,
ISVC20(II:570-580).
Springer DOI
2103
Graph visualization.
BibRef
El Bouchairi, I.[Imad],
El Moataz, A.[Abderrahim],
Fadili, J.[Jalal],
Discrete p-bilaplacian Operators on Graphs,
ICISP20(339-347).
Springer DOI
2009
BibRef
de Ita, G.[Guillermo],
Rodríguez, M.[Miguel],
Bello, P.[Pedro],
Contreras, M.[Meliza],
Basic Pattern Graphs for the Efficient Computation of Its Number of
Independent Sets,
MCPR20(57-66).
Springer DOI
2007
BibRef
Song, J.,
Andres, B.,
Black, M.,
Hilliges, O.,
Tang, S.,
End-to-End Learning for Graph Decomposition,
ICCV19(10092-10101)
IEEE DOI
2004
convolutional neural nets, graph theory,
learning (artificial intelligence), optimisation
BibRef
Karantaidis, G.[George],
Sarridis, I.[Ioannis],
Kotropoulos, C.[Constantine],
Block Randomized Optimization for Adaptive Hypergraph Learning,
ICIP19(864-868)
IEEE DOI
1910
Adaptive hypergraph learning, Randomized algorithms,
Block randomized singular value decomposition, Conjugate gradient method
BibRef
Zanfir, A.,
Sminchisescu, C.,
Deep Learning of Graph Matching,
CVPR18(2684-2693)
IEEE DOI
Award, CVPR, HM.
1812
Feature extraction, Computational modeling, Symmetric matrices,
Optimization, Mathematical model
BibRef
Yu, T.S.[Tian-Shu],
Yan, J.C.[Jun-Chi],
Zhao, J.Y.[Jie-Yi],
Li, B.X.[Bao-Xin],
Joint Cuts and Matching of Partitions in One Graph,
CVPR18(705-713)
IEEE DOI
1812
Task analysis, Standards, Partitioning algorithms,
Optimization, Transportation, Pattern matching
BibRef
Benaya, N.,
El-Akchioui, N.,
Mourabit, T.,
Limits of fluidification for a stochastic Petri nets by timed
continuous Petri nets,
ISCV18(1-7)
IEEE DOI
1807
Markov processes, Petri nets, continuous systems,
discrete event systems, reliability, stochastic processes,
stochastic Petri nets
BibRef
López-Ramírez, C.[Cristina],
de Ita, G.[Guillermo],
Neri, A.[Alfredo],
Modelling 3-Coloring of Polygonal Trees via Incremental Satisfiability,
MCPR18(93-102).
Springer DOI
1807
BibRef
Salim, A.[Asif],
Shiju, S.S.,
Sumitra, S.,
Effectiveness of Representation and Length Variation of Shortest Paths
in Graph Classification,
PReMI17(509-516).
Springer DOI
1711
BibRef
Lê-Huu, D.K.[D. Khuê],
Paragios, N.[Nikos],
Alternating Direction Graph Matching,
CVPR17(4914-4922)
IEEE DOI
1711
Convex functions, Linear programming,
Pattern matching, Tensile, stress
BibRef
Swoboda, P.,
Rother, C.,
Alhaija, H.A.,
Kainmüller, D.,
Savchynskyy, B.,
A Study of Lagrangean Decompositions and Dual Ascent Solvers for
Graph Matching,
CVPR17(7062-7071)
IEEE DOI
1711
Labeling, Message passing, Optimization,
Pattern matching, Schedules
BibRef
Tang, M.[Min],
Wang, W.M.[Wen-Min],
Progressive Probabilistic Graph Matching with Local Consistency
Regularization,
CAIP17(II: 105-115).
Springer DOI
1708
BibRef
Song, D.B.[Dao-Bang],
Zhang, J.W.[Jiu-Wen],
Zhou, J.[Jing],
Case study for graph signal denoising by graph structure similarity,
ICIVC17(847-851)
IEEE DOI
1708
Additives, Gaussian noise, Noise measurement,
Partitioning algorithms, Silicon carbide, community detection,
denoising, graph signal processing, graph structure similarity,
image modeling.
BibRef
Rossi, L.[Luca],
Severini, S.[Simone],
Torsello, A.[Andrea],
The Average Mixing Matrix Signature,
SSSPR16(474-484).
Springer DOI
1611
signatures for graphs, used for matching.
BibRef
Lockhart, J.[Joshua],
Minello, G.[Giorgia],
Rossi, L.[Luca],
Severini, S.[Simone],
Torsello, A.[Andrea],
Edge Centrality via the Holevo Quantity,
SSSPR16(143-152).
Springer DOI
1611
most important edge.
BibRef
de Ita, G.[Guillermo],
Bello, P.[Pedro],
Contreras, M.[Meliza],
Catana-Salazar, J.C.[Juan C.],
Efficient Counting of the Number of Independent Sets on Polygonal Trees,
MCPR16(167-176).
Springer DOI
1608
BibRef
Yu, T.,
Wang, R.,
Graph matching with low-rank regularization,
WACV16(1-9)
IEEE DOI
1606
Linear matrix inequalities
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Ye, C.[Cheng],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Correlation Network Evolution Using Mean Reversion Autoregression,
SSSPR16(163-173).
Springer DOI
1611
BibRef
Minello, G.[Giorgia],
Torsello, A.[Andrea],
Hancock, E.R.[Edwin R.],
Quantum thermodynamics of time evolving networks,
ICPR16(1536-1541)
IEEE DOI
1705
BibRef
And:
Thermodynamic Characterization of Temporal Networks,
SSSPR16(49-59).
Springer DOI
1611
Correlation, Eigenvalues and eigenfunctions, Energy exchange,
Entropy, Laplace equations, Stock markets, Thermodynamics
BibRef
Ye, C.[Cheng],
Torsello, A.[Andrea],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Thermodynamics of Time Evolving Networks,
GbRPR15(315-324).
Springer DOI
1511
BibRef
Oskarsson, M.[Magnus],
Astrom, K.[Kalle],
Torstensson, A.[Anna],
Prime Rigid Graphs and Multidimensional Scaling with Missing Data,
ICPR14(750-755)
IEEE DOI
1412
Bipartite graph
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Brimkov, B.[Boris],
On Sets of Line Segments Featuring a Cactus Structure,
IWCIA17(30-39).
Springer DOI
1706
BibRef
Earlier:
Memory Efficient Shortest Path Algorithms for Cactus Graphs,
ISVC13(I:476-485).
Springer DOI
1310
BibRef
Raetz, W.,
A new approach to graph analysis for activity based intelligence,
AIPR12(1-8)
IEEE DOI
1307
graph theory
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Ji, Y.M.[Yi-Ming],
Yu, C.B.[Chang-Bin],
Anderson, B.D.O.,
Threshold phenomenon for average consensus,
ICARCV12(548-553).
IEEE DOI
1304
BibRef
Suh, Y.M.[Yu-Min],
Cho, M.S.[Min-Su],
Lee, K.M.[Kyoung Mu],
Graph Matching via Sequential Monte Carlo,
ECCV12(III: 624-637).
Springer DOI
1210
BibRef
Yan, J.C.[Jun-Chi],
Zhang, C.[Chao],
Zha, H.Y.[Hong-Yuan],
Liu, W.[Wei],
Yang, X.K.[Xiao-Kang],
Chu, S.M.[Stephen M.],
Discrete hyper-graph matching,
CVPR15(1520-1528)
IEEE DOI
1510
BibRef
Yan, J.C.[Jun-Chi],
Tian, Y.[Yu],
Zha, H.Y.[Hong-Yuan],
Yang, X.K.[Xiao-Kang],
Zhang, Y.[Ya],
Chu, S.M.[Stephen M.],
Joint Optimization for Consistent Multiple Graph Matching,
ICCV13(1649-1656)
IEEE DOI
1403
BibRef
Tian, Y.[Yu],
Yan, J.C.[Jun-Chi],
Zhang, H.Q.[He-Quan],
Zhang, Y.[Ya],
Yang, X.K.[Xiao-Kang],
Zha, H.Y.[Hong-Yuan],
On the Convergence of Graph Matching: Graduated Assignment Revisited,
ECCV12(III: 821-835).
Springer DOI
1210
BibRef
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ICIP08(2368-2371).
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ICIAR08(xx-yy).
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
General Structure and Graph Representation, Relations, Neighbors .