13.3.12.13 Graph Clustering, Cilque Generation

Chapter Contents (Back)
Graph Clustering.

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

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


Fernández-Menduińa, S.[Samuel], Pavez, E.[Eduardo], Ortega, A.[Antonio],
Image Coding Via Perceptually Inspired Graph Learning,
ICIP23(2495-2499)
IEEE DOI 2312
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 .


Last update:Mar 16, 2024 at 20:36:19