Rizzi, S.[Stefano],
Genetic operators for hierarchical graph clustering,
PRL(19), No. 14, December 1998, pp. 1293-1300.
BibRef
9812
Bunke, H.,
Inexact Graph Matching for Structural Pattern Recognition,
PRL(1), No. 4, 1983, pp. 245-253.
BibRef
8300
Günter, S.[Simon],
Bunke, H.[Horst],
Self-organizing map for clustering in the graph domain,
PRL(23), No. 4, February 2002, pp. 405-417.
Elsevier DOI
0202
BibRef
Günter, S.[Simon],
Bunke, H.[Horst],
Validation indices for graph clustering,
PRL(24), No. 8, May 2003, pp. 1107-1113.
Elsevier DOI
0304
BibRef
Zanghi, H.[Hugo],
Ambroise, C.[Christophe],
Miele, V.[Vincent],
Fast online graph clustering via Erdos-Renyi mixture,
PR(41), No. 12, December 2008, pp. 3592-3599.
Elsevier DOI
0810
EM algorithm; Graph clustering; Online
BibRef
Zanghi, H.[Hugo],
Volant, S.[Stevenn],
Ambroise, C.[Christophe],
Clustering based on random graph model embedding vertex features,
PRL(31), No. 9, 1 July 2010, pp. 830-836.
Elsevier DOI
1004
Variational EM algoritm; Graph clustering; Vertex features
BibRef
Rota Bulň, S.[Samuel],
Pelillo, M.[Marcello],
A Game-Theoretic Approach to Hypergraph Clustering,
PAMI(35), No. 6, June 2013, pp. 1312-1327.
IEEE DOI
1305
Extract coherent groups using high-order (not pairwise) similarities.
BibRef
Wang, F.D.[Fu-Dong],
Xue, N.[Nan],
Zhang, Y.P.[Yi-Peng],
Xia, G.S.[Gui-Song],
Pelillo, M.[Marcello],
A Functional Representation for Graph Matching,
PAMI(42), No. 11, November 2020, pp. 2737-2754.
IEEE DOI
2010
Strain, Linear programming, Time complexity, Measurement,
Optimization, Pattern matching, Graph matching,
geometric deformation
BibRef
Hou, J.[Jian],
Yuan, H.Q.[Hua-Qiang],
Pelillo, M.[Marcello],
Game-theoretic hypergraph matching with density enhancement,
PR(133), 2023, pp. 109035.
Elsevier DOI
2210
Feature matching, Hypergraph matching, Game-theoretic, Density enhancement
BibRef
Hou, J.[Jian],
Pelillo, M.[Marcello],
Yuan, H.Q.[Hua-Qiang],
Hypergraph matching via game-theoretic hypergraph clustering,
PR(125), 2022, pp. 108526.
Elsevier DOI
2203
BibRef
Earlier: A1, A2, Only:
A Game-Theoretic Hyper-Graph Matching Algorithm,
ICPR18(1012-1017)
IEEE DOI
1812
Feature matching, Hypergraph matching, Game-theoretic, Hypergraph clustering.
Games, Clustering algorithms, Nash equilibrium, Pattern matching,
Visualization, Tensile stress, Partitioning algorithms
BibRef
Hou, J.[Jian],
Qi, N.M.[Nai-Ming],
Efficient Game-Theoretic Hypergraph Matching,
ICPR21(4213-4220)
IEEE DOI
2105
Tensors, Clustering algorithms, Games, Robustness, Pattern matching
BibRef
Kontschieder, P.[Peter],
Rota Bulo, S.[Samuel],
Pelillo, M.[Marcello],
Bischof, H.[Horst],
Structured Labels in Random Forests for Semantic Labelling and Object
Detection,
PAMI(36), No. 10, October 2014, pp. 2104-2116.
IEEE DOI
1410
BibRef
Earlier: A2, A1, A3, A4:
Structured Local Predictors for image labelling,
CVPR12(3530-3537).
IEEE DOI
1208
BibRef
Earlier: A1, A2, A4, A3:
Structured class-labels in random forests for semantic image labelling,
ICCV11(2190-2197).
IEEE DOI
1201
Structural information in Random Forest framework.
BibRef
Wu, J.,
Pan, S.,
Zhu, X.,
Cai, Z.,
Boosting for Multi-Graph Classification,
Cyber(45), No. 3, March 2015, pp. 430-443.
IEEE DOI
1502
Algorithm design and analysis
BibRef
Pan, S.,
Wu, J.,
Zhu, X.,
Zhang, C.,
Graph Ensemble Boosting for Imbalanced Noisy Graph Stream
Classification,
Cyber(45), No. 5, May 2015, pp. 940-954.
IEEE DOI
1505
Accuracy
BibRef
Tahaei, M.S.[Maedeh S.],
Hashemi, S.N.[Seyed Naser],
Graph Characterization by Counting Sink Star Subgraphs,
JMIV(57), No. 3, March 2017, pp. 439-454.
Springer DOI
1702
BibRef
Pelillo, M.[Marcello],
Elezi, I.[Ismail],
Fiorucci, M.[Marco],
Revealing structure in large graphs:
Szemerédi's regularity lemma and its use in pattern recognition,
PRL(87), No. 1, 2017, pp. 4-11.
Elsevier DOI
1703
Graph-theoretic methods
BibRef
Meng, Z.[Zhaoyi],
Merkurjev, E.[Ekaterina],
Koniges, A.[Alice],
Bertozzi, A.L.[Andrea L.],
Hyperspectral Image Classification Using Graph Clustering Methods,
IPOL(7), 2017, pp. 218-245.
DOI Link
1708
Code, Hyperspectral Classification. Initial description:
See also Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme.
See also Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video. Parallel Implementation:
See also OpenMP parallelization and optimization of graph-based machine learning algorithms.
BibRef
Qin, Y.K.[Yi-Kun],
Yu, Z.L.[Zhu Liang],
Wang, C.D.[Chang-Dong],
Gu, Z.H.[Zheng-Hui],
Li, Y.Q.[Yuan-Qing],
A Novel clustering method based on hybrid K-nearest-neighbor graph,
PR(74), No. 1, 2018, pp. 1-14.
Elsevier DOI
1711
Graph clustering
BibRef
Zhan, K.,
Nie, F.,
Wang, J.,
Yang, Y.,
Multiview Consensus Graph Clustering,
IP(28), No. 3, March 2019, pp. 1261-1270.
IEEE DOI
1812
graph theory, iterative methods, matrix algebra, optimisation,
pattern clustering, unsupervised learning, optimization problem,
graph learning
BibRef
Huang, S.D.[Shu-Dong],
Kang, Z.[Zhao],
Tsang, I.W.[Ivor W.],
Xu, Z.L.[Zeng-Lin],
Auto-weighted multi-view clustering via kernelized graph learning,
PR(88), 2019, pp. 174-184.
Elsevier DOI
1901
Graph learning, Multi-view clustering,
Multiple kernel learning, Auto-weighted strategy
BibRef
Huang, S.D.[Shu-Dong],
Kang, Z.[Zhao],
Xu, Z.L.[Zeng-Lin],
Auto-weighted multi-view clustering via deep matrix decomposition,
PR(97), 2020, pp. 107015.
Elsevier DOI
1910
Multi-view learning, Deep matrix decomposition, Clustering,
Optimization algorithm
BibRef
Wu, S.[Song],
Zheng, Y.[Yan],
Ren, Y.Z.[Ya-Zhou],
He, J.[Jing],
Pu, X.R.[Xiao-Rong],
Huang, S.D.[Shu-Dong],
Hao, Z.F.[Zhi-Feng],
He, L.F.[Li-Fang],
Self-Weighted Contrastive Fusion for Deep Multi-View Clustering,
MultMed(26), 2024, pp. 9150-9162.
IEEE DOI
2409
Self-supervised learning, Matrix decomposition,
Task analysis, Symbols, Semantics, Representation learning,
representation degeneration
BibRef
Lu, X.M.[Xiao-Min],
Yan, H.[Haowen],
Li, W.[Wende],
Li, X.J.[Xiao-Jun],
Wu, F.[Fang],
An Algorithm based on the Weighted Network Voronoi Diagram for Point
Cluster Simplification,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link
1903
Clustering using the roads that connect the points (towns).
BibRef
Araghi, H.,
Sabbaqi, M.,
Babaie-Zadeh, M.,
K-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph
Learning,
SPLetters(26), No. 10, October 2019, pp. 1486-1490.
IEEE DOI
1909
Clustering algorithms, Signal processing algorithms,
Laplace equations, Symmetric matrices, Estimation,
graph Laplacian matrix
BibRef
Kim, Y.[Younghoon],
Do, H.[Hyungrok],
Kim, S.B.[Seoung Bum],
Outer-Points shaver: Robust graph-based clustering via node cutting,
PR(97), 2020, pp. 107001.
Elsevier DOI
1910
Graph-based clustering, Unsupervised learning,
Spectral clustering, Pseudo-density reconstruction, Node cutting
BibRef
Wang, R.,
Nie, F.,
Wang, Z.,
He, F.,
Li, X.,
Scalable Graph-Based Clustering With Nonnegative Relaxation for Large
Hyperspectral Image,
GeoRS(57), No. 10, October 2019, pp. 7352-7364.
IEEE DOI
1910
computational complexity, eigenvalues and eigenfunctions,
geophysical image processing, graph theory,
nonnegative relaxation
BibRef
Fan, X.L.[Xiao-Long],
Gong, M.[Maoguo],
Xie, Y.[Yu],
Jiang, F.L.[Fen-Long],
Li, H.[Hao],
Structured self-attention architecture for graph-level representation
learning,
PR(100), 2020, pp. 107084.
Elsevier DOI
2005
Neural self-attention mechanism, Graph neural networks, Graph classification
BibRef
Wu, T.[Tong],
Graph regularized low-rank representation for submodule clustering,
PR(100), 2020, pp. 107145.
Elsevier DOI
2005
Clustering, Kernel methods, Manifold regularization,
Submodule clustering, Tensor nuclear norm, Union of free submodules
BibRef
He, T.,
Liu, Y.,
Ko, T.H.,
Chan, K.C.C.,
Ong, Y.S.,
Contextual Correlation Preserving Multiview Featured Graph Clustering,
Cyber(50), No. 10, October 2020, pp. 4318-4331.
IEEE DOI
2009
Correlation, Context modeling, Optimization,
Social networking (online), Computational modeling, Topology,
multiview features
BibRef
Chang, J.Y.[Jing-Ya],
Chen, Y.N.[Yan-Nan],
Qi, L.Q.[Li-Qun],
Yan, H.[Hong],
Hypergraph Clustering Using a New Laplacian Tensor with Applications
in Image Processing,
SIIMS(13), No. 3, 2020, pp. 1157-1178.
DOI Link
2010
BibRef
Poulin, V.[Valérie],
Théberge, F.[François],
Comparing Graph Clusterings: Set Partition Measures vs. Graph-Aware
Measures,
PAMI(43), No. 6, June 2021, pp. 2127-2132.
IEEE DOI
2106
Partitioning algorithms, Indexes, Clustering algorithms,
Mutual information, Size measurement, Topology,
partition similarity
BibRef
Hua, J.L.[Jia-Lin],
Yu, J.[Jian],
Yang, M.S.[Miin-Shen],
Star-based learning correlation clustering,
PR(116), 2021, pp. 107966.
Elsevier DOI
2106
Correlation clustering, Graphs, Integer linear program (ILP),
Star-based learning correlation clustering (SL-CC), Signed network
BibRef
Tan, J.P.[Jun-Peng],
Yang, Z.J.[Zhi-Jing],
Cheng, Y.Q.[Yong-Qiang],
Ye, J.L.[Jie-Lin],
Wang, B.[Bing],
Dai, Q.Y.[Qing-Yun],
SRAGL-AWCL: A two-step multi-view clustering via sparse
representation and adaptive weighted cooperative learning,
PR(117), 2021, pp. 107987.
Elsevier DOI
2106
Multi-view clustering, Sparse representation (sr),
Adaptive graph learning (agl), Global Optimized Matrix
BibRef
Huang, D.X.[Da-Xin],
Jiang, J.Z.[Jun-Zheng],
Zhou, F.[Fang],
Ouyang, S.[Shan],
A distributed algorithm for graph semi-supervised learning,
PRL(151), 2021, pp. 48-54.
Elsevier DOI
2110
Graph semi-supervised learning (GSSL),
Graph signal processing, Laplacian, Distributed algorithm
BibRef
Wang, C.[Chun],
Pan, S.R.[Shi-Rui],
Yu, C.P.[Celina P.],
Hu, R.[Ruiqi],
Long, G.D.[Guo-Dong],
Zhang, C.Q.[Cheng-Qi],
Deep neighbor-aware embedding for node clustering in attributed graphs,
PR(122), 2022, pp. 108230.
Elsevier DOI
2112
Attributed graph, Node clustering, Graph attention network,
Graph convolutional network, Network representation
BibRef
Wu, D.Y.[Dan-Yang],
Chang, W.[Wei],
Lu, J.[Jitao],
Nie, F.P.[Fei-Ping],
Wang, R.[Rong],
Li, X.L.[Xue-Long],
Adaptive-order proximity learning for graph-based clustering,
PR(126), 2022, pp. 108550.
Elsevier DOI
2204
Graph-based clustering, Structured proximity matrix learning,
High-order proximity, Adaptive learning
BibRef
Strazzeri, F.[Fabio],
Sánchez-García, R.J.[Rubén J.],
Possibility results for graph clustering: A novel consistency axiom,
PR(128), 2022, pp. 108687.
Elsevier DOI
2205
Data clustering, Graph clustering, Axiomatic clustering,
Morse theory, Morse flow
BibRef
Zhang, T.[Tao],
Shan, H.R.[Hao-Ran],
Little, M.A.[Max A.],
Causal GraphSAGE: A robust graph method for classification based on
causal sampling,
PR(128), 2022, pp. 108696.
Elsevier DOI
2205
Causal GraphSAGE, GraphSAGE, Causal sampling, Robustness, Causal inference
BibRef
Douik, A.[Ahmed],
Hassibi, B.[Babak],
Low-Rank Riemannian Optimization for Graph-Based Clustering
Applications,
PAMI(44), No. 9, September 2022, pp. 5133-5148.
IEEE DOI
2208
Optimization, Manifolds, Symmetric matrices, Geometry,
Clustering algorithms, Tools, Search problems,
convex and non-convex optimization
BibRef
Li, W.[Wang],
Wang, S.W.[Si-Wei],
Guo, X.F.[Xi-Feng],
Zhu, E.[En],
Deep graph clustering with multi-level subspace fusion,
PR(134), 2023, pp. 109077.
Elsevier DOI
2212
Graph clustering, Subspace, Self-expressive learning, Fusion
BibRef
Li, X.F.[Xing-Feng],
Ren, Z.W.[Zhen-Wen],
Sun, Q.S.[Quan-Sen],
Xu, Z.[Zhi],
Auto-weighted Tensor Schatten p-Norm for Robust Multi-view Graph
Clustering,
PR(134), 2023, pp. 109083.
Elsevier DOI
2212
Multi-view clustering, Adaptive neighbors graph learning,
Low-rank tensor learning, Noise estimation
BibRef
Karimi, P.[Parisa],
Butala, M.D.[Mark D.],
Zhao, Z.Z.[Zhi-Zhen],
Kamalabadi, F.[Farzad],
Efficient Model Selection in Switching Linear Dynamic Systems by
Graph Clustering,
SPLetters(29), 2022, pp. 2482-2486.
IEEE DOI
2212
Superluminescent diodes, Trajectory, Switches,
Heuristic algorithms, Dynamical systems, Covariance matrices, graph clustering
BibRef
Dong, Z.[Zhe],
Wang, Q.L.[Qi-Long],
Zhu, P.F.[Peng-Fei],
Multi-head second-order pooling for graph transformer networks,
PRL(167), 2023, pp. 53-59.
Elsevier DOI
2303
Graph transformer networks, Second-order pooling, Graph classification
BibRef
Li, H.R.[Hao-Ran],
Guo, Y.L.[Yu-Lan],
Ren, Z.W.[Zhen-Wen],
Yu, F.R.[F. Richard],
You, J.L.[Jia-Li],
You, X.J.[Xiao-Jian],
Explicit Local Coupling Global Structure Clustering,
CirSysVideo(33), No. 11, November 2023, pp. 6649-6660.
IEEE DOI
2311
BibRef
Peng, Z.H.[Zhi-Hao],
Liu, H.[Hui],
Jia, Y.H.[Yu-Heng],
Hou, J.H.[Jun-Hui],
EGRC-Net: Embedding-Induced Graph Refinement Clustering Network,
IP(32), 2023, pp. 6457-6468.
IEEE DOI Code:
WWW Link.
2312
BibRef
Hajiveiseh, A.[Akram],
Seyedi, S.A.[Seyed Amjad],
Tab, F.A.[Fardin Akhlaghian],
Deep asymmetric nonnegative matrix factorization for graph clustering,
PR(148), 2024, pp. 110179.
Elsevier DOI
2402
Nonnegative matrix factorization, Deep learning,
Graph clustering, Directed graph
BibRef
Mrabah, N.[Nairouz],
Bouguessa, M.[Mohamed],
Ksantini, R.[Riadh],
A contrastive variational graph auto-encoder for node clustering,
PR(149), 2024, pp. 110209.
Elsevier DOI
2403
Unsupervised learning, Contrastive learning,
Graph variational auto-encoders, Node clustering
BibRef
Liu, C.[Chang],
Zhang, H.B.[Hong-Bing],
Fan, H.T.[Hong-Tao],
Li, Y.J.[Ya-Jing],
Tensorial bipartite graph clustering based on logarithmic coupled
penalty,
PR(156), 2024, pp. 110860.
Elsevier DOI
2408
Multi-view clustering, Bipartite graph,
Logarithmic coupled penalty, Theoretical convergence
BibRef
Liu, Y.[Ye],
Lin, X.[Xuelei],
Chen, Y.[Yejia],
Cheng, R.[Reynold],
Multi-order graph clustering with adaptive node-level weight learning,
PR(156), 2024, pp. 110843.
Elsevier DOI
2408
Graph clustering, Motifs, Higher-order structure,
Spectral clustering, Optimization
BibRef
Wang, S.W.[Si-Wei],
Liu, X.W.[Xin-Wang],
Liu, S.[Suyuan],
Tu, W.X.[Wen-Xuan],
Zhu, E.[En],
Scalable and Structural Multi-View Graph Clustering With Adaptive
Anchor Fusion,
IP(33), 2024, pp. 4627-4639.
IEEE DOI Code:
WWW Link.
2409
Complexity theory, Clustering algorithms, Fuses,
Clustering methods, Periodic structures, Benchmark testing, anchor graph
BibRef
Wu, D.Y.[Dan-Yang],
Yang, Z.K.[Zhen-Kun],
Lu, J.[Jitao],
Xu, J.[Jin],
Xu, X.M.[Xiang-Min],
Nie, F.P.[Fei-Ping],
EBMGC-GNF: Efficient Balanced Multi-View Graph Clustering via Good
Neighbor Fusion,
PAMI(46), No. 12, December 2024, pp. 7878-7892.
IEEE DOI
2411
Adaptation models, Computational modeling, Task analysis, Kernel,
Clustering algorithms, Proposals, Minimization,
balanced clustering
BibRef
Xu, Y.H.[Yu-Hua],
Wang, J.L.[Jun-Li],
Guang, M.J.[Ming-Jian],
Jiang, C.J.[Chang-Jun],
Graph Multi-Convolution and Attention Pooling for Graph
Classification,
PAMI(46), No. 12, December 2024, pp. 10546-10557.
IEEE DOI
2411
Convolution, Task analysis, Feature extraction, Aggregates, Vectors,
Semantics, Attention mechanisms, Attention mechanism,
weight-based aggregation
BibRef
Chen, C.[Chao],
Geng, H.Y.[Hao-Yu],
Yang, N.Z.[Nian-Zu],
Yang, X.K.[Xiao-Kang],
Yan, J.C.[Jun-Chi],
EasyDGL: Encode, Train and Interpret for Continuous-Time Dynamic
Graph Learning,
PAMI(46), No. 12, December 2024, pp. 10845-10862.
IEEE DOI
2411
Task analysis, Predictive models, Training, Forecasting, Encoding,
Time-domain analysis, Data models, Continuous-time dynamic graph,
temporal point process
BibRef
Takanami, K.[Keigo],
Bandoh, Y.[Yukihiro],
Takamura, S.[Seishi],
Tanaka, Y.[Yuichi],
Multimodadl Graph Signal Denoising With Simultaneous Graph Learning
using Deep Algorithm Unrolling,
ICIP23(1555-1559)
IEEE DOI
2312
BibRef
Zhu, W.B.[Wen-Bin],
Wang, C.Y.[Chien-Yi],
Tseng, K.L.[Kuan-Lun],
Lai, S.H.[Shang-Hong],
Wang, B.Y.[Bao-Yuan],
Local-Adaptive Face Recognition via Graph-based Meta-Clustering and
Regularized Adaptation,
CVPR22(20269-20278)
IEEE DOI
2210
Training, Adaptation models, Data privacy, Protocols, Convolution,
Face recognition, Biometrics, Face and gestures
BibRef
Zhong, H.S.[Hua-Song],
Wu, J.L.[Jian-Long],
Chen, C.[Chong],
Huang, J.Q.[Jian-Qiang],
Deng, M.H.[Ming-Hua],
Nie, L.Q.[Li-Qiang],
Lin, Z.C.[Zhou-Chen],
Hua, X.S.[Xian-Sheng],
Graph Contrastive Clustering,
ICCV21(9204-9213)
IEEE DOI
2203
Learning systems, Laplace equations, Correlation,
Clustering methods, Benchmark testing, Task analysis, Representation learning
BibRef
Geng, Z.Q.[Zhi-Qiang],
Li, Z.K.[Zhong-Kun],
Han, Y.M.[Yong-Ming],
A Novel Asymmetric Embedding Model for Knowledge Graph Completion,
ICPR18(290-295)
IEEE DOI
1812
Space vehicles, Orbits, Training, Complexity theory,
Knowledge engineering, Benchmark testing, Predictive models,
Asymmetrical Embedding
BibRef
Niu, J.H.[Jing-Hao],
Sun, Z.Y.[Zheng-Ya],
Zhang, W.S.[Wen-Sheng],
Enhancing Knowledge Graph Completion with Positive Unlabeled Learning,
ICPR18(296-301)
IEEE DOI
1812
Reliability, Logistics, Predictive models, Correlation, Semantics,
Data models, Training
BibRef
Ikami, D.,
Yamasaki, T.,
Aizawa, K.,
Local and Global Optimization Techniques in Graph-Based Clustering,
CVPR18(3456-3464)
IEEE DOI
1812
Cost function, Optimization methods, Linear programming,
Sparse matrices, Clustering algorithms
BibRef
Flores-Garrido, M.[Marisol],
Carrasco-Ochoa, J.A.[Jesús Ariel],
Martínez-Trinidad, J.F.[José F.],
Graph Clustering via Inexact Patterns,
CIARP14(391-398).
Springer DOI
1411
BibRef
García-Borroto, M.[Milton],
Villuendas-Rey, Y.[Yenny],
Carrasco-Ochoa, J.A.[Jesús Ariel],
Martínez-Trinidad, J.F.[José F.],
Finding Small Consistent Subset for the Nearest Neighbor Classifier
Based on Support Graphs,
CIARP09(465-472).
Springer DOI
0911
BibRef
García-Borroto, M.[Milton],
Villuendas-Rey, Y.[Yenny],
Carrasco-Ochoa, J.A.[Jesús Ariel],
Martínez-Trinidad, J.F.[José F.],
Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers,
CIARP09(489-496).
Springer DOI
0911
BibRef
Suárez, A.P.[Airel Pérez],
Trinidad, J.F.M.[José F. Martínez],
Carrasco Ochoa, J.A.[Jesús A.],
Medina Pagola, J.E.[José E.],
A New Incremental Algorithm for Overlapped Clustering,
CIARP09(497-504).
Springer DOI
0911
BibRef
Tan, M.K.[Ming-Kui],
Shi, Q.F.[Qin-Feng],
van den Hengel, A.J.[Anton J.],
Shen, C.H.[Chun-Hua],
Gao, J.B.[Jun-Bin],
Hu, F.Y.[Fu-Yuan],
Zhang, Z.[Zhen],
Learning Graph Structure for Multi-Label Image Classification Via
Clique Generation,
CVPR15(4100-4109)
IEEE DOI
1510
BibRef
Donoser, M.[Michael],
Replicator Graph Clustering,
BMVC13(xx-yy).
DOI Link
1402
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Graph Embedding Clustering .