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Sadamori Kojaku
Sadamori Kojaku
Binghamton University
Keine bestätigte E-Mail-Adresse - Startseite
Titel
Zitiert von
Zitiert von
Jahr
The effectiveness of backward contact tracing in networks
S Kojaku, L Hébert-Dufresne, E Mones, S Lehmann, YY Ahn
Nature physics 17 (5), 652-658, 2021
133*2021
Finding multiple core-periphery pairs in networks
S Kojaku, N Masuda
Physical Review E 96 (5), 052313, 2017
672017
Core-periphery structure requires something else in the network
S Kojaku, N Masuda
New Journal of physics 20 (4), 043012, 2018
622018
Configuration model for correlation matrices preserving the node strength
N Masuda, S Kojaku, Y Sano
Physical Review E 98 (1), 012312, 2018
372018
Multiscale core-periphery structure in a global liner shipping network
S Kojaku, M Xu, H Xia, N Masuda
Scientific reports 9 (1), 404, 2019
302019
A generalised significance test for individual communities in networks
S Kojaku, N Masuda
Scientific reports 8 (1), 7351, 2018
222018
Detecting anomalous citation groups in journal networks
S Kojaku, G Livan, N Masuda
Scientific reports 11 (1), 14524, 2021
212021
Constructing networks by filtering correlation matrices: A null model approach
S Kojaku, N Masuda
Proceedings of the Royal Society A 475 (2231), 20190578, 2019
212019
Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis
S Kojaku, G Cimini, G Caldarelli, N Masuda
arXiv preprint arXiv:1802.05139, 2018
192018
Fast random k-labelsets for large-scale multi-label classification
K Kimura, M Kudo, L Sun, S Koujaku
2016 23rd International Conference on Pattern Recognition (ICPR), 438-443, 2016
192016
Residual2Vec: Debiasing graph embedding with random graphs
S Kojaku, J Yoon, I Constantino, YY Ahn
Advances in Neural Information Processing Systems 34, 24150-24163, 2021
142021
Unsupervised embedding of trajectories captures the latent structure of mobility
D Murray, J Yoon, S Kojaku, R Costas, WS Jung, S Milojević, YY Ahn
arXiv preprint arXiv:2012.02785, 2020
142020
Dense core model for cohesive subgraph discovery
S Koujaku, I Takigawa, M Kudo, H Imai
Social Networks 44, 143-152, 2016
122016
Structual change point detection for evolutional networks
S Koujaku, M Kudo, I Takigawa, H Imai
Proceedings of the World Congress on Engineering 1, 324-329, 2013
102013
Prediction method, prediction system and program
S Kojaku, T Morimura, T Osogami, R Takahaski
US Patent 9,087,294, 2015
82015
Community change detection in dynamic networks in noisy environment
S Koujaku, M Kudo, I Takigawa, H Imai
Proceedings of the 24th International Conference on World Wide Web, 793-798, 2015
72015
Solving feature sparseness in text classification using core-periphery decomposition
X Cui, S Kojaku, N Masuda, D Bollegala
Proceedings of the Seventh Joint Conference on Lexical and Computational …, 2018
62018
Venture Capital Networks: An analysis using the exponential random graph model
S Koujaku, D Miyakawa
RIETI, 2017
52017
Network community detection via neural embeddings
S Kojaku, F Radicchi, YY Ahn, S Fortunato
arXiv preprint arXiv:2306.13400, 2023
32023
Unsupervised embedding of trajectories captures the latent structure of scientific migration
D Murray, J Yoon, S Kojaku, R Costas, WS Jung, S Milojević, YY Ahn
Proceedings of the National Academy of Sciences 120 (52), e2305414120, 2023
12023
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