# 13.3 Graph Matching and Relaxation

Chapter Contents (Back)
Object Recognition. Matching, Graphs. Graph Matching.

## 13.3.1 Graph Matching Theoretical Issues

Chapter Contents (Back)
Constraint Satisfaction. Matching, Graphs. Graph Matching, Theory. Generally these papers restrict the problem to an exact matching problem, which is the easy case. Also discrete relaxation:

Corneil, D.G., and Gotlieb, C.C.,
An Efficient Algorithm for Graph Isomorphism,
JACM(17), No. 1, January 1970, pp. 51-64. Graph Isomorphism. This technique for isomorphism derived two graphs from the input graphs. The original graphs are isomorphic only if the derived graphs are identical. The algorithm, complexity O(N^n ) is more efficient than the early renumbering techniques that were O(N!), because the power is usually no more than 5 (for some special cases, usually it is lower). This technique applies to non-directed graphs and was not extended to bi-directional graphs, except as a part of an O(N!) algorithm. BibRef 7001

Shaw, A.C.,
Parsing of Graph-Representable Pictures,
JACM(17), No. 3, July 1970, pp. 453-481. BibRef 7007

Shaw, A.C.[Alan C.],
A Formal Picture Description Scheme as a Basis for Picture Processing Systems,
InfoControl(14), No. 1, January 1969, pp. 9-52. BibRef 6901

Mallgren, W.R., Shaw, A.C.,
Graphical transformations and hierarchic picture structures,
CGIP(8), No. 2, October 1978, pp. 237-258.
Elsevier DOI 0501
BibRef

Hopcroft, J.E., and Tarjan, R.E.,
Isomorphism of Planar Graphs,
CCComp(131-152). 1972. BibRef 7200

Tarjan, R.E.,
Depth First Search and Linear Graph Algorithms,
SIAM_JC(1), No. 1, 1972, pp 146-160. BibRef 7200

Pfaltz, J.L.,
Graph Structures,
JACM(19), No. 3, July 1972, pp. 411-422. BibRef 7207

Berztiss, A.T.,
A Backtrack Procedure for Isomorphism of Directed Graphs,
JACM(20), No. 3, July, 1973, pp. 365-372. Graph Isomorphism. This algorithm, applied to directed graphs, first transforms the graph representation into a linear string that describes the connections between nodes. These strings start from strings describing each node and combine to form a single string describing the entire graph. From the combination rules a matching string is formed by a tree search procedure that adds one node at a time and retains only those possible strings that match the pattern. This technique proved effective for most real graphs even though it does not escape the possible worst cases. BibRef 7307

Ullmann, J.R.,
A Consistency Technique for Pattern Association,
IT(8), No. 5, September 1962, pp. 74-81. Relaxation, Discrete. Describes a simple hardware implementation, lacks an abstract mathematical formulation -- hardware is easier than software in 1962. BibRef 6209

Ullmann, J.R.,
Distributive Implementation of Relational Operations,
IEE-P(E: 137), No. 4, July 1990, pp. 283-294. Updated version of original paper. BibRef 9007

Ullmann, J.R.,
Parallel Recognition of Idealised Line Characters,
Kybernetic(2), Part 5, June 1965, pp. 221-226. Original discrete relaxation with mathematical fomulation. This paper is available:
PDF File. Also from the Springer site:
Springer DOI It has been made available with kind permission of Springer Science+Business Media, A short version of this paper is available:
PDF File.

Ullmann, J.R.,
A Use of Continutiy in Character Recognition,
SMC(4), No. 3, May 1974, pp. 294-300. Using binary constraint propogation, can a character be a distorted image of another character.
See also Recognition experiments with typed numerals from envelopes in the mail. BibRef 7405

Ullmann, J.R.,
Subset Methods for Recognising Distorted Patterns,
SMC(7), No. 3, March 1977, pp. 180-191. Non-binary constraint propogation, not all boundaries need to be closed. BibRef 7703

Ullmann, J.R.,
Pattern Recognition Using Degenerate Reference Data,
PRAI-76(508-528). Distorted patterns compared to reference patterns. Non-binary propogation. BibRef 7600

Berge, C.,
Graphs and Hypergraphs,
North HollandAmsterdam, 1973. BibRef 7300

Osteen, R., and Tou, J.T.,
A Clique-Directed Algorithm Based on Neighborhoods in Graphs,
CIS(2), No. 4, December 1973, pp. 257-268. BibRef 7312

van Scoy, F.L.,
The Parallel Recognition of Classes of Graphs,
TC(29), 1980, pp. 563-570. BibRef 8000

Ghahraman, D.E., Wong, A.K.C., Au, T.,
Graph Optimal Monomorphism Algorithm,
SMC(10), April 1980, pp. 181-188. BibRef 8004

Ghahraman, D.E., Wong, A.K.C., Au, T.,
Graph Monomorphism Algorithms,
SMC(10), 1980, pp. 189-196. BibRef 8000

Forgy, C.L.,
Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem,
AI(19), No. 1, September 1982, pp. 17-37.
Elsevier DOI Precompute the network to make matching fast. Intended for production rule matching. BibRef 8209

Fowler, G., Haralick, R.M., Gray, F.G., Feustel, C., and Grinstead, C.,
Efficient Graph Automorphism by Vertex Partitioning,
AI(21), No. 1-2, March 1983, pp. 245-269.
Elsevier DOI BibRef 8303

Quinn, M.J., Deo, N.,
Parallel Graph Algorithms,
Surveys(16), No. 3, September 1984, pp. 319-348. Survey, Graph Matching. BibRef 8409

Nyo, H.L., Suk, M.,
A Polynomial Time Algorithm for Subpattern Matching,
PIEEE(74), 1986, pp. 375-377. BibRef 8600

Galil, Z.,
Efficient Algorithms for Finding Maximum Matching in Graphs,
Surveys(18), No. 1, March 1986, pp. 23-38.

Thathachar, M.A.L., and Sastry, P.S.,
Relaxation Labeling with Learning Automata,
PAMI(8), No. 2, March 1986, pp. 256-268. BibRef 8603

Davies, E.R.,
Alternative to Abstract Graph Matching for Locating Objects from Their Salient Features,
IVC(9), No. 4, August 1991, pp. 252-261.
Elsevier DOI BibRef 9108

A Linear Programming Approach fo the Weighted Graph Matching Problem,
PAMI(15), No. 5, May 1993, pp. 522-525.
IEEE DOI Linear Programming. BibRef 9305

Seong, D.S., Choi, Y.K., Kim, H.S., Park, K.H.,
An Algorithm For Optimal Isomorphism Between 2 Random Graphs,
PRL(15), No. 4, April 1994, pp. 321-327. BibRef 9404

Jeavons, P.G.[Peter G.], Coope, M.C.[Martin C.],
Tractable Constraints on Ordered Domains,
AI(79), No. 2, January 1996, pp. 327-339.
Elsevier DOI By ordering the domain it is possible to limit the constraints to pairwise constraints, then get a linear solution. Used in
See also Linear-time algorithms for testing the realisability of line drawings of curved objects. BibRef 9601

Pedrycz, W.,
Classification of Relational Patterns as a Decomposition Problem,
PRL(17), No. 1, January 10 1996, pp. 91-99. BibRef 9601

Pedrycz, W.,
Neurocomputations in relational systems,
PAMI(13), No. 3, March 1991, pp. 289-297.
IEEE DOI 0401
BibRef

Cucka, P., Netanyahu, N.S., and Rosenfeld, A.,
Learning in Navigation: Goal Finding in Graphs,
PRAI(10), 1996, pp. 429-446. BibRef 9600

Jumarie, G.,
Informational Similarity of Graphs in Syntactic Pattern Recognition,
PRL(15), 1994, pp. 1177-1181. BibRef 9400

Owolabi, O.,
A Graph Canonization Method For Pattern Recognition,
PRL(12), 1991, pp. 701-705. BibRef 9100

Seong, D.S., Kim, H.S., Park, K.H.,
Incremental Clustering of Attributed Graphs,
SMC(23), 1993, pp. 1399-1411. BibRef 9300

Goldman, R.P., Charniak, E.,
A language for construction of belief networks,
PAMI(15), No. 3, March 1993, pp. 196-208.
IEEE DOI 0401
BibRef

Buckley, M., Yang, J.,
Regularized Shortest Path Extraction,
PRL(18), No. 7, July 1997, pp. 621-629. 9711
Shortest path through a graph. BibRef

Barbehenn, M.,
A Note on the Complexity of Dijkstras Algorithm for Graphs with Weighted Vertices,
TC(47), No. 2, February 1998, pp. 263-263. 9803
BibRef

Bunke, H.,
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function,
PAMI(21), No. 9, September 1999, pp. 917-922.
IEEE DOI For any cost function, there are an infinite number of others that lead to the same optimal error correcting matching. BibRef 9909

Miller, D.A.[Douglas A.], Zucker, S.W.[Steven W.],
Cliques, computation, and computational tractability,
PR(33), No. 4, April 2000, pp. 535-542.
Elsevier DOI 0002
BibRef

Luo, B., Hancock, E.R.,
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition,
PAMI(23), No. 10, October 2001, pp. 1120-1136.
IEEE DOI 0110
BibRef
Earlier:
Symbolic Graph Matching Using the EM Algorithm and Singular Value Decomposition,
ICPR00(Vol II: 141-144).
IEEE DOI 0009

Dickinson, S.J.[Sven J.], Pelillo, M.[Marcello], Zabih, R.[Ramin],
Introduction to the Special Section on Graph Algorithms and Computer Vision,
PAMI(23), No. 10, October 2001, pp. 1049-1052.
IEEE DOI 0110
BibRef

Ostergard, P.,
A Fast Algorithm for the Maximum Clique Problem,
DiscAppMath(120), 2002, pp. 197-207. BibRef 0200

Raphael, C.[Christopher],
Coarse-to-Fine Dynamic Programming,
PAMI(23), No. 12, December 2001, pp. 1379-1390.
IEEE DOI 0112
Dynamic Programming. Applied to mine detection. Generalize dynamic programming to hierarchical system. BibRef

Multihierarchical Graph Search,
PAMI(24), No. 1, January 2002, pp. 103-113.
IEEE DOI 0201
Applied to path planning. BibRef

Cordella, L.P., Foggia, P., Sansone, C., Vento, M.,
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs,
PAMI(26), No. 10, October 2004, pp. 1367-1372.
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], Saggese, A.[Alessia], Vento, M.[Mario],
Challenging the Time Complexity of Exact Subgraph Isomorphism for Huge and Dense Graphs with VF3,
PAMI(40), No. 4, April 2018, pp. 804-818.
IEEE DOI 1804
BibRef
Earlier:
Introducing VF3: A New Algorithm for Subgraph Isomorphism,
GbRPR17(128-139).
Springer DOI 1706
Algorithm design and analysis, Biology, Complexity theory, Heuristic algorithms, Memory management, Search problems, subgraph isomorphism BibRef

Conte, D.[Donatello], Foggia, P.[Pasquale], Jolion, J.M.[Jean-Michel], Vento, M.[Mario],
A graph-based, multi-resolution algorithm for tracking objects in presence of occlusions,
PR(39), No. 4, April 2006, pp. 562-572.
Elsevier DOI 0604
Object tracking; Occlusion problem; Graph pyramid; Multi-resolution segmentation BibRef

Foggia, P.[Pasquale], Percannella, G.[Gennaro], Sansone, C.[Carlo], Vento, M.[Mario],
Benchmarking graph-based clustering algorithms,
IVC(27), No. 7, 4 June 2009, pp. 979-988.
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 BibRef

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], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Eigenspaces For Graphs,
IJIG(2), No. 2, April 2002, pp. 247-268. 0204
BibRef

Luo, B.[Bin], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
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

Luo, B.[Bin], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
A Spectral Approach to Learning Structural Variations in Graphs,
PR(39), No. 6, June 2006, pp. 1188-1198.
Elsevier DOI 0604
BibRef
Earlier: CVS03(407 ff).
Springer DOI 0306
BibRef
Earlier:
Learning modes of structural variation in graphs,
ICIP03(II: 37-40).
IEEE DOI 0312
BibRef
Earlier:
Spectral Clustering of Graphs,

Springer DOI 0311
Generative model; Graph; Covariance matrix; Clustering BibRef

Wilson, R.C.[Richard C.],
Graph Signatures for Evaluating Network Models,
ICPR14(100-105)
IEEE DOI 1412
Biological system modeling BibRef

Han, L.[Lin], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Generative Graph Prototypes from Information Theory,
PAMI(37), No. 10, October 2015, pp. 2013-2027.
IEEE DOI 1509
Complexity theory BibRef

Luo, B.[Bin], Robles-Kelly, A.[Antonio], Torsello, A.[Andrea], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
A Probabilistic Framework for Graph Clustering,
CVPR01(I:912-919).
IEEE DOI 0110
From set of distances between graphs characterize pairwise affinity. Culuster graphs. BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Graph matching and clustering using spectral partitions,
PR(39), No. 1, January 2006, pp. 22-34.
Elsevier DOI 0512
BibRef
Earlier:
Spectral Simplification of Graphs,
ECCV04(Vol IV: 114-126).
Springer DOI 0405
BibRef

Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.], Luo, B.[Bin],
Pattern Vectors from Algebraic Graph Theory,
PAMI(27), No. 7, July 2005, pp. 1112-1124.
IEEE Abstract. 0506
BibRef
Earlier: A2, A3, A1:
Graph Pattern Spaces from Laplacian Spectral Polynomials,
ICIAR04(I: 327-334).
Springer DOI 0409
BibRef
Earlier: A1, A2, Only:
Pattern Spaces from Graph Polynomials,
CIAP03(480-485).
IEEE DOI 0310
Embed graphs in pattern space. BibRef

Falcao, A.X.[Alexandre X.], Stolfi, J.[Jorge], de Alencar Lotufo, R.[Roberto],
The Image Foresting Transform: Theory, Algorithms, and Applications,
PAMI(26), No. 1, January 2004, pp. 19-29.
IEEE Abstract. 0401
Graph-based design of image processing operators using connectivity. Minimum-cost path forest in a graph. BibRef

van Wyk, B.J.[Barend J.], and van Wyk, M.A.[Michael A.],
A POCS-Based Graph Matching Algorithm,
PAMI(26), No. 11, November 2004, pp. 1526-1530.
IEEE Abstract. 0410
Projections onto Convex Sets. Solve Attributed Graph Matching. BibRef

Wang, H.F.[Hong Fang], Hancock, E.R.[Edwin R.],
Correspondence matching using kernel principal components analysis and label consistency constraints,
PR(39), No. 6, June 2006, pp. 1012-1025.
Elsevier DOI 0604
BibRef
Earlier:
Improving Correspondence Matching Using Label Consistency Constraints,
IbPRIA05(I:235).
Springer DOI 0509
BibRef
And:
Kernel Spectral Correspondence Matching Using Label Consistency Constraints,
CIAP05(503-510).
Springer DOI 0509
Non-rigid motion; Correspondence matching; Graph spectral methods; Kernel PCA; Constraints BibRef

Bunke, H.[Horst], Dickinson, P.[Peter], Irniger, C.[Christophe], Kraetzl, M.[Miro],
Recovery of missing information in graph sequences by means of reference pattern matching and decision tree learning,
PR(39), No. 4, April 2006, pp. 573-586.
Elsevier DOI 0604
Graph sequence analysis; Recovery of missing information; Computer network analysis; Machine learning; Decision tree classifier; Reference pattern matching BibRef

Brun, L.[Luc], Escolano, F.[Francisco],
Graph-based Representations Preface,
IVC(27), No. 7, 4 June 2009, pp. 835-836.
Elsevier DOI 0904
BibRef

Bonev, B.[Boyan], Lozano, M.A.[Miguel A.], Escolano, F.[Francisco], Suau, P.[Pablo], Aguilar, W.[Wendy], Saez, J.M., Cazorla, M.A.[Miguel A.],
Region and constellations based categorization of images with unsupervised graph learning,
IVC(27), No. 7, 4 June 2009, pp. 960-978.
Elsevier DOI 0904
Image categorization; Clustering of graphs; EM algorithms BibRef
Earlier: A3, A2, A1, A4, A7, A5, Only:
Constellations and the Unsupervised Learning of Graphs,
GbRPR07(340-350).
Springer DOI 0706
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

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], 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

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.
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.
BibRef

Leordeanu, M.[Marius], Hebert, M.[Martial],
Pairwise Grouping Using Color,
CMU-RI-TR-08-46, December, 2008.

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, Pattern recognition, 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.
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.
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., Vogelstein, J.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.
New algorithm for graph isomorphism.

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.
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

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

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.
Dealing with enormous amounts of data. 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

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

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

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

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, Pattern recognition 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, Pattern recognition, 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.
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

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

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, Market research, Graph theory BibRef

Ma, X.Y.[Xiao-Yan], Liu, L.[Ling], Yuan, W.[Wei], Zhang, Y.X.[Yue-Xiu], Song, L.[Lianjun],
Summary of Static Graph Embedding Algorithms,
CVIDL23(404-411)
IEEE DOI 2403
Dimensionality reduction, Deep learning, Neural networks, Market research, Matrix decomposition. 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

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

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

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

Yu, T., Wang, R.,
Graph matching with low-rank regularization,
WACV16(1-9)
IEEE DOI 1606
Linear matrix inequalities 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

Bunke, H.,
Recent Developments in Graph Matching,
ICPR00(Vol II: 117-124).
IEEE DOI 0009
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
General Structure and Graph Representation, Relations, Neighbors .

Last update:Jul 18, 2024 at 20:50:34