13.3 Graph Matching and Relaxation

13.3.1 Graph Matching Theoretical Issues

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

Corneil, D.G., and Kirkpatrick, D.G.,
A Theoretical Analysis of Various Heuristics for the Graph Isomorphism Problem,
SIAM_JC(9), 1980, pp. 281-297. BibRef 8000

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.
See also Algorithm for Subgraph Isomorphism, An. BibRef 6506

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

Bron, C., and Kerbosch, J.,
Algorithm 457: Finding All Cliques in an Undirected Graph (H),
CACM(16), 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

Bhat, K.V.S.,
Refined Vertex Codes and Vertex Partitioning Methodology for Graph Isomorphism Testing,
SMC(10), 1980, pp. 610-615. 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

Mehlhorn, K.,
Data Structure and Algorithms 2: Graphs Algorithms and NP-Completeness,
Springer-Verlag1984. BibRef 8400 Book BibRef

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.
See also String Matching in Real Time. BibRef 8603

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.,
The Minimal Match Graph and its Use to Speed Identification of Maximal Cliques,
SP(22), 1991, pp. 329-343. BibRef 9100

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

Simic, P.,
Constrained Nets for Graph matching and Other Quadratic Assignment Problems,
NeurComp(3), 1991, pp. 268-291. BibRef 9100

Almohamad, H.A., and Duffuaa, S.O.,
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

Suganthan, P.N., Yan, H., Teoh, E.K., Mital, D.P.,
Optimal Encoding of Graph Homomorphism Energy Using Fuzzy Information Aggregation Operators,
PR(31), No. 5, May 1998, pp. 623-639.
Elsevier DOI 9805
BibRef

Abdulrahim, M.[Mohammad], Misra, M.[Manavendra],
A Graph Isomorphism Algorithm for Object Recognition,
PAA(1), No. 3, 1998, pp. xx-yy. BibRef 9800

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

See also Multiple Line-Template Matching with the EM Algorithm.
See also Registering Incomplete Radar Images Using the EM Algorithm. BibRef

Luo, B., Hancock, E.R.,
A robust eigen-decomposition framework for inexact graph-matching,
CIAP01(465-470).
IEEE DOI 0210
BibRef

Carcassoni, M.[Marco], Hancock, E.R.[Edwin R.],
Weighted Graph-Matching Using Modal Clusters,
CAIP01(142 ff.).
Springer DOI 0210
BibRef

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

Wang, J.T.L.[Jason T.L.], Zhang, K.Z.[Kai-Zhong], Chang, G.[George], Shasha, D.[Dennis],
Finding approximate patterns in undirected acyclic graphs,
PR(35), No. 2, February 2002, pp. 473-483.
Elsevier DOI 0201
BibRef

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

Fernandez-Madrigal, J.A.[Juan-Antonio], Gonzalez, J.[Javier],
Multihierarchical Graph Search,
PAMI(24), No. 1, January 2002, pp. 103-113.
IEEE DOI 0201
Applied to path planning. BibRef

de Santo, M., Foggia, P., Sansone, C., Vento, M.,
A large database of graphs and its use for benchmarking graph isomorphism algorithms,
PRL(24), No. 8, May 2003, pp. 1067-1079.
Elsevier DOI 0304
BibRef

Cordella, L.P., Sansone, C., Tortorella, F., Vento, M.[Mario], Foggia, P.,
Graph Matching: A Fast Algorithm and its Evaluation,
ICPR98(Vol II: 1582-1584).
IEEE DOI 9808
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], Vento, M.[Mario], Jiang, X.Y.[Xiao-Yi],
Report on the First Contest on Graph Matching Algorithms for Pattern Search in Biological Databases,
GbRPR15(178-187).
Springer DOI 1511
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

Carletti, V.[Vincenzo], Foggia, P.[Pasquale], Vento, M.[Mario],
Performance Comparison of Five Exact Graph Matching Algorithms on Biological Databases,
PR-PS-BB13(409-417).
Springer DOI 1309
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

Raveaux, R.[Romain], Martineau, M.[Maxime], Conte, D.[Donatello], Venturini, G.[Gilles],
Learning Graph Matching with a Graph-Based Perceptron in a Classification Context,
GbRPR17(49-58).
Springer DOI 1706
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

Wilson, R.C., Hancock, E.R.,
Levenshtein distance for graph spectral features,
ICPR04(II: 489-492).
IEEE DOI 0409
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

Luo, B.[Bin], Wilson, R.C., Hancock, E.R.,
The independent and principal component of graph spectra,
ICPR02(II: 164-167).
IEEE DOI 0211
BibRef

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

Aziz, F.[Furqan], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Graph Entropy from Closed Walk and Cycle Functionals,
SSSPR16(174-184).
Springer DOI 1611
BibRef

Ye, C.[Cheng], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
An Entropic Edge Assortativity Measure,
GbRPR15(23-33).
Springer DOI 1511
BibRef
And:
Graph Characterization from Entropy Component Analysis,
ICPR14(3845-3850)
IEEE DOI 1412
BibRef
Earlier:
Entropic Graph Embedding via Multivariate Degree Distributions,
SSSPR14(163-172).
Springer DOI 1408
BibRef
And:
Heterogeneity Index for Directed Graphs,
CAIP13(II:424-431).
Springer DOI 1311
Complexity theory BibRef

Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio],
An Energy Minimisation Approach to Attributed Graph Regularisation,
EMMCVPR07(71-86).
Springer DOI 0708
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

Han, L.[Lin], Rossi, L.[Luca], Torsello, A.[Andrea], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Information Theoretic Prototype Selection for Unattributed Graphs,
SSSPR12(33-41).
Springer DOI 1211
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

Han, L.[Lin], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.], Bai, L.[Lu], Ren, P.[Peng],
Sampling graphs from a probabilistic generative model,
ICPR12(1643-1646).
WWW Link. 1302
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

Bunke, H.[Horst], Irniger, C.[Christophe], Neuhaus, M.[Michel],
Graph Matching: Challenges and Potential Solutions,
CIAP05(1-10).
Springer DOI 0509
BibRef

Bunke, H.[Horst], Dickinson, P.[Peter], Kraetzl, M.[Miro],
Theoretical and Algorithmic Framework for Hypergraph Matching,
CIAP05(463-470).
Springer DOI 0509
BibRef
And:
Comparison of Two Different Prediction Schemes for the Analysis of Time Series of Graphs,
IbPRIA05(II:99).
Springer DOI 0509
BibRef

Huang, R.H.[Rui-Hong],
A Schedule-based Pathfinding Algorithm for Transit Networks Using Pattern First Search,
GeoInfo(11), No. 2, June 2007, pp. 269-285.
Springer DOI 0709
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

Romero, A.[Anna], Cazorla, M.A.[Miguel A.],
Topological SLAM Using Omnidirectional Images: Merging Feature Detectors and Graph-Matching,
ACIVS10(I: 464-475).
Springer DOI 1012
BibRef

Escolano, F.[Francisco], Liu, M.[Meizhu], Hancock, E.R.[Edwin R.],
Tensor-based total Bregman divergences between graphs,
ITCVPR11(1440-1447).
IEEE DOI 1201
BibRef

Escolano, F.[Francisco], Hancock, E.R.[Edwin R.],
Bragg Diffraction Patterns as Graph Characteristics,
EMMCVPR17(62-75).
Springer DOI 1805
BibRef

Escolano, F.[Francisco], Hancock, E.R.[Edwin R.],
Complexity Fusion for Indexing Reeb Digraphs,
CAIP13(120-127).
Springer DOI 1308
BibRef
And:
From Points to Nodes: Inverse Graph Embedding through a Lagrangian Formulation,
CAIP11(I: 194-201).
Springer DOI 1109
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
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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
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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
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Earlier: A1, A3, A2:
Birkhoff polytopes, heat kernels and graph complexity,
ICPR08(1-5).
IEEE DOI 0812
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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
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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
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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
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Leordeanu, M.[Marius], Hebert, M.[Martial],
Unsupervised learning for graph matching,
CVPR09(864-871).
IEEE DOI 0906
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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
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Leordeanu, M.[Marius], Hebert, M.[Martial],
A Spectral Technique for Correspondence Problems Using Pairwise Constraints,
ICCV05(II: 1482-1489).
IEEE DOI 0510
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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, 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.
WWW Link. 1311
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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
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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
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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
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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
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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

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, 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.
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
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Park, J.D.[Jin-Duk], Tran, C.[Cong], Shin, W.Y.[Won-Yong], Cao, X.[Xin],
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PAMI(45), No. 12, December 2023, pp. 15292-15307.
IEEE DOI 2311
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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

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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
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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

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
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IWCIA22(301-316).
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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

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On the Correspondence Between Replicator Dynamics and Assignment Flows,
SSVM21(373-384).
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smooth dynamical systems for data labeling on graphs. BibRef

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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

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End-to-End Learning for Graph Decomposition,
ICCV19(10092-10101)
IEEE DOI 2004
convolutional neural nets, graph theory, learning (artificial intelligence), optimisation BibRef

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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],
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Effectiveness of Representation and Length Variation of Shortest Paths in Graph Classification,
PReMI17(509-516).
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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],
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ICIVC17(847-851)
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SSSPR16(474-484).
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WACV16(1-9)
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Correlation Network Evolution Using Mean Reversion Autoregression,
SSSPR16(163-173).
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Quantum thermodynamics of time evolving networks,
ICPR16(1536-1541)
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Thermodynamic Characterization of Temporal Networks,
SSSPR16(49-59).
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Correlation, Eigenvalues and eigenfunctions, Energy exchange, Entropy, Laplace equations, Stock markets, Thermodynamics BibRef

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Thermodynamics of Time Evolving Networks,
GbRPR15(315-324).
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Prime Rigid Graphs and Multidimensional Scaling with Missing Data,
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Memory Efficient Shortest Path Algorithms for Cactus Graphs,
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A new approach to graph analysis for activity based intelligence,
AIPR12(1-8)
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graph theory BibRef

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ICARCV12(548-553).
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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).
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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,
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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)
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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 .