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Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for clinical diagnostic applications.
S. Ogawa, T.-M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proceedings of the National Academy of Sciences , vol. 87, no. 24, pp. 9868–9872, 1990
1990
Earlier work this paper cites.
J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, “Signature verification using a” siamese” time delay neural network,” in NIPS , 1994, pp. 737–744
1994
Earlier work this paper cites.
J. Shi and J. Malik, “Normalized cuts and image segmentation,” Departmental Papers (CIS) , p. 107, 2000
2000
Earlier work this paper cites.
N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain,” Neuroimage , vol. 15, no. 1, pp. 273–289, 2002
2002
Earlier work this paper cites.
D. D. Cox and R. L. Savoy, “Functional magnetic resonance imaging (fmri)“brain reading”: detecting and classifying distributed patterns of fmri activity in human visual cortex,” Neuroimage , vol. 19, no. 2, pp. 261–270, 2003
2003
Earlier work this paper cites.
S. Chopra, R. Hadsell, and Y. LeCun, “Learning a similarity metric discriminatively, with application to face verification,” in CVPR , vol. 1, 2005, pp. 539–546
2005
Earlier work this paper cites.
U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and computing , vol. 17, no. 4, pp. 395–416, 2007
2007
Earlier work this paper cites.
M. P. Van Den Heuvel and H. E. H. Pol, “Exploring the brain network: a review on resting-state fmri functional connectivity,” European neuropsychopharmacology , vol. 20, no. 8, pp. 519–534, 2010
2010
Earlier work this paper cites.
O. Sporns, “The human connectome: a complex network,” Annals of the New York Academy of Sciences , vol. 1224, no. 1, pp. 109–125, 2011
2011
Earlier work this paper cites.
M. Rubinov and O. Sporns, “Weight-conserving characterization of complex functional brain networks,” Neuroimage , vol. 56, no. 4, pp. 2068–2079, 2011
2011
Earlier work this paper cites.
D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,” Applied and Computational Harmonic Analysis , vol. 30, no. 2, pp. 129–150, 2011
2011
Earlier work this paper cites.
A. B. Ragin, H. Du, R. Ochs, Y. Wu, C. L. Sammet, A. Shoukry, and L. G. Epstein, “Structural brain alterations can be detected early in hiv infection,” Neurology , vol. 79, no. 24, pp. 2328–2334, 2012
2012
Earlier work this paper cites.
S. Whitfield-Gabrieli and A. Nieto-Castanon, “Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks,” Brain connectivity , vol. 2, no. 3, pp. 125–141, 2012
2012
Earlier work this paper cites.
O. Sporns, “Structure and function of complex brain networks,” Dialogues in clinical neuroscience , vol. 15, no. 3, p. 247, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” IEEE Signal Processing Magazine , vol. 30, no. 3, pp. 83–98, 2013
2013
Cited alongside, same era.
X. Kong and P. S. Yu, “Brain network analysis: a data mining perspective,” ACM SIGKDD Explorations Newsletter , vol. 15, no. 2, pp. 30–38, 2014
2014
Cited alongside, same era.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” pp. 701–710, 2014. [Online]. Available: http://doi.acm.org/10.1145/2623330.2623732
2014
Cited alongside, same era.
C. Robert, “Machine learning, a probabilistic perspective,” 2014
2014
Cited alongside, same era.
S. Wang, L. He, B. Cao, C.-T. Lu, P. S. Yu, and A. B. Ragin, “Structural deep brain network mining,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2017, pp. 475–484
2017
Later among the works it cites.
G. Ma, C.-T. Lu, L. He, S. Y. Philip, and A. B. Ragin, “Multi-view graph embedding with hub detection for brain network analysis,” in Data Mining (ICDM), 2017 IEEE International Conference on . IEEE, 2017, pp. 967–972
2017
Later among the works it cites.
G. Ma, L. He, C.-T. Lu, W. Shao, P. S. Yu, A. D. Leow, and A. B. Ragin, “Multi-view clustering with graph embedding for connectome analysis,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 2017, pp. 127–136
2017
Later among the works it cites.
I. Cribben and Y. Yu, “Estimating whole-brain dynamics by using spectral clustering,” Journal of the Royal Statistical Society: Series C (Applied Statistics) , vol. 66, no. 3, pp. 607–627, 2017
2017
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2014
Cited alongside, same era.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML Deep Learning Workshop , vol. 2, 2015
2015
Cited alongside, same era.
B. Cao, L. Zhan, X. Kong, S. Y. Philip, N. Vizueta, L. L. Altshuler, and A. D. Leow, “Identification of discriminative subgraph patterns in fmri brain networks in bipolar affective disorder,” in International Conference on Brain Informatics and Health . Springer, 2015, pp. 105–114
2015
Cited alongside, same era.
B. Cao, X. Kong, J. Zhang, P. S. Yu, and A. B. Ragin, “Identifying hiv-induced subgraph patterns in brain networks with side information,” Brain informatics , vol. 2, no. 4, pp. 211–223, 2015
2015
Cited alongside, same era.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in Neural Information Processing Systems , 2016, pp. 3844–3852
2016
Cited alongside, same era.
2016
Cited alongside, same era.
G. Ma, L. He, B. Cao, J. Zhang, P. S. Yu, and A. B. Ragin, “Multi-graph clustering based on interior-node topology with applications to brain networks,” in ECML PKDD . Springer, 2016, pp. 476–492
2016
Cited alongside, same era.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” 2016
2016
Cited alongside, same era.
Later among the works it cites.
J. Liu, M. Li, Y. Pan, W. Lan, R. Zheng, F.-X. Wu, and J. Wang, “Complex brain network analysis and its applications to brain disorders: a survey,” Complexity , vol. 2017, 2017
2017
Later among the works it cites.
Z. Bai, P. Walker, A. Tschiffely, F. Wang, and I. Davidson, “Unsupervised network discovery for brain imaging data,” in SIGKDD , 2017, pp. 55–64
2017
Later among the works it cites.
W. Zhang, K. Shu, S. Wang, H. Liu, and Y. Wang, “Multimodal fusion of brain networks with longitudinal couplings,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 3–11
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Z. Bai, B. Qian, and I. Davidson, “Discovering models from structural and behavioral brain imaging data,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2018, pp. 1128–1137
2018
Later among the works it cites.
S. I. Ktena, S. Parisot, E. Ferrante, M. Rajchl, M. Lee, B. Glocker, and D. Rueckert, “Metric learning with spectral graph convolutions on brain connectivity networks,” NeuroImage , vol. 169, pp. 431–442, 2018
2018
Later among the works it cites.
G. Ma, N. K. Ahmed, T. L. Willke, D. Sengupta, M. W. Cole, N. B. Turk-Browne, and P. S. Yu, “Deep graph similarity learning for brain data analysis,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management . ACM, 2019, pp. 2743–2751
2019
Closest in time.
R. Liao, Z. Zhao, R. Urtasun, and R. S. Zemel, “Lanczosnet: Multi-scale deep graph convolutional networks,” ICLR , 2019
2019
Closest in time.
M. Qu, Y. Bengio, and J. Tang, “Gmnn: Graph markov neural networks,” ICML , 2019
2019
Closest in time.