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 | 142* | 2021 |
Finding multiple core-periphery pairs in networks S Kojaku, N Masuda Physical Review E 96 (5), 052313, 2017 | 80 | 2017 |
Core-periphery structure requires something else in the network S Kojaku, N Masuda New Journal of physics 20 (4), 043012, 2018 | 70 | 2018 |
Configuration model for correlation matrices preserving the node strength N Masuda, S Kojaku, Y Sano Physical Review E 98 (1), 012312, 2018 | 39 | 2018 |
Multiscale core-periphery structure in a global liner shipping network S Kojaku, M Xu, H Xia, N Masuda Scientific reports 9 (1), 404, 2019 | 32 | 2019 |
Detecting anomalous citation groups in journal networks S Kojaku, G Livan, N Masuda Scientific Reports 11 (1), 14524, 2021 | 27 | 2021 |
A generalised significance test for individual communities in networks S Kojaku, N Masuda Scientific reports 8 (1), 7351, 2018 | 23 | 2018 |
Constructing networks by filtering correlation matrices: A null model approach S Kojaku, N Masuda Proceedings of the Royal Society A 475 (2231), 20190578, 2019 | 22 | 2019 |
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 | 19 | 2018 |
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 | 19 | 2016 |
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 | 18 | 2023 |
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 | 18 | 2021 |
Dense core model for cohesive subgraph discovery S Koujaku, I Takigawa, M Kudo, H Imai Social Networks 44, 143-152, 2016 | 13 | 2016 |
Prediction method, prediction system and program S Kojaku, T Morimura, T Osogami, R Takahaski US Patent 9,087,294, 2015 | 10 | 2015 |
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 | 10 | 2013 |
Network community detection via neural embeddings S Kojaku, F Radicchi, YY Ahn, S Fortunato arXiv preprint arXiv:2306.13400, 2023 | 8 | 2023 |
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 | 7 | 2015 |
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 | 6 | 2018 |
Venture Capital Networks: An analysis using the exponential random graph model S Koujaku, D Miyakawa RIETI, 2017 | 4 | 2017 |
A rationally oriented forgettable profit sharing S Koujaku, K Watanabe, H Igarashi Electronics and Communications in Japan 96 (7), 11-18, 2013 | 1 | 2013 |