Preprints

• Atsushi Miyauchi, Florian Adriaens, Francesco Bonchi, and Nikolaj Tatti:

Multilayer correlation clustering,

arXiv:2404.16676

• Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, and Mark Herbster:

Bandits with abstention under expert advice,

arXiv:2402.14585.

• Yuko Kuroki, Atsushi Miyauchi, Francesco Bonchi, and Wei Chen:

Query-efficient correlation clustering with noisy oracle,

arXiv:2402.01400.

Refereed Conference Proceedings

• Atsushi Miyauchi, Lorenzo Severini, and Francesco Bonchi:

Local centrality minimization with quality guarantees,

Proceedings of The ACM Web Conference 2024 (TheWebConf 2024), pp. 410–421.

Acceptance rate: 18.4% (Graph Algorithms and Modeling for the Web Track)

• Atsushi Miyauchi, Tianyi Chen, Konstantinos Sotiropoulos, and Charalampos E. Tsourakakis:

Densest diverse subgraphs: How to plan a successful cocktail party with diversity,

Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023), pp. 1710–1721.

Acceptance rate: 313/1416 = 22.2% (Research Track)

• Yasushi Kawase, Atsushi Miyauchi, and Hanna Sumita: 

Stochastic solutions for dense subgraph discovery in multilayer networks, 

Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM 2023), pp. 886–894.

Acceptance rate: 123/690 = 17.9%

• Tianyi Chen, Francesco Bonchi, David García-Soriano, Atsushi Miyauchi, and Charalampos E. Tsourakakis: 

Dense and well-connected subgraph detection in dual networks,

Proceedings of the 22nd SIAM International Conference on Data Mining (SDM 2022), pp. 361–369.

Acceptance rate: 83/298 = 27.9%

• Naoki Marumo, Atsushi Miyauchi, Akiko Takeda, and Akira Tanaka: 

A projected gradient method for opinion optimization with limited changes of susceptibility to persuasion, 

Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), pp. 1274–1283.

Acceptance rate: 271/1251 = 21.7% (Full Papers)

• Yuko Kuroki, Atsushi Miyauchi, Junya Honda, and Masashi Sugiyama:

Online dense subgraph discovery via blurred-graph feedback, 

Proceedings of the 37th International Conference on Machine Learning (ICML 2020), pp. 5522–5532.

Acceptance rate: 1088/4990 = 21.9%

• Yuuki Takai, Atsushi Miyauchi, Masahiro Ikeda, and Yuichi Yoshida:

Hypergraph clustering based on PageRank, 

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020), pp. 1970–1978.

Acceptance rate: 216/1279 = 16.9% (Research Track)

• Yasushi Kawase, Yuko Kuroki, and Atsushi Miyauchi:

Graph mining meets crowdsourcing: Extracting experts for answer aggregation, 

Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), pp. 1272–1279.

Acceptance rate: 850/4752 = 17.9%

• Atsushi Miyauchi and Akiko Takeda: 

Robust densest subgraph discovery, 

Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), pp. 1188–1193.

Acceptance rate: 189/948 = 20.0%

• Atsushi Miyauchi and Naonori Kakimura: 

Finding a dense subgraph with sparse cut, 

Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), pp. 547–556. 

Acceptance rate: 147/862 = 17.1% (Full Paper Track)

• Atsushi Miyauchi, Tomohiro Sonobe, and Noriyoshi Sukegawa:

Exact clustering via integer programming and maximum satisfiability,

Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), pp. 1387–1394.

Acceptance rate: 933/3800+ = 24.6%

• Yasushi Kawase and Atsushi Miyauchi:

The densest subgraph problem with a convex/concave size function,

Proceedings of the 27th International Symposium on Algorithms and Computation (ISAAC 2016), pp. 44:1–44:12.

Acceptance rate: 62/155 = 40.0%

• Yasushi Kawase, Tomomi Matsui, and Atsushi Miyauchi:

Additive approximation algorithms for modularity maximization,

Proceedings of the 27th International Symposium on Algorithms and Computation (ISAAC 2016), pp. 43:1–43:13.

Acceptance rate: 62/155 = 40.0%

• Atsushi Miyauchi and Yasushi Kawase:

What is a network community? A novel quality function and detection algorithms,

Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), pp. 1471–1480.

Acceptance rate: 87/346 = 25.1% (Knowledge Management Track, Long Paper)

• Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, and Naonori Kakimura:

Threshold influence model for allocating advertising budgets,

Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 1395–1404.

Acceptance rate: 270/1037 = 26.0%

Refereed Journal Articles

• Tommaso Lanciano, Atsushi Miyauchi, Adriano Fazzone, and Francesco Bonchi: 

A survey on the densest subgraph problem and its variants, 

ACM Computing Surveys 56, pp. 1–40, 2024.

• Issey Sukeda, Atsushi Miyauchi, and Akiko Takeda:

A study on modularity density maximization: Column generation acceleration and computational complexity analysis,

European Journal of Operational Research 309, pp. 516–528, 2023. 

• Masahiro Ikeda, Atsushi Miyauchi, Yuuki Takai, and Yuichi Yoshida: 

Finding Cheeger cuts in hypergraphs via heat equation,

Theoretical Computer Science 930, pp. 1–23, 2022.

• Francesco Bonchi, David García-Soriano, Atsushi Miyauchi, and Charalampos E. Tsourakakis: 

Finding densest k-connected subgraphs, 

Discrete Applied Mathematics 305, pp. 34–47, 2021. 

• Yasushi Kawase, Tomomi Matsui, and Atsushi Miyauchi:

Additive approximation algorithms for modularity maximization, 

Journal of Computer and System Sciences 117, pp. 182–201, 2021. 

• Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, and Masashi Sugiyama:

Polynomial-time algorithms for multiple-arm identification with full-bandit feedback,

Neural Computation 32, pp. 1733–1773, 2020.

• Yasushi Kawase and Atsushi Miyauchi:

The densest subgraph problem with a convex/concave size function,

Algorithmica 80, pp. 3461–3480, 2018.

• Atsushi Miyauchi and Yasushi Kawase: 

Z-score-based modularity for community detection in networks,

PLOS ONE 11, e0147805 (17 pages), 2016.

• Atsushi Miyauchi and Noriyoshi Sukegawa:

Maximizing Barber's bipartite modularity is also hard,

Optimization Letters 9, pp. 897–913, 2015.

• Atsushi Miyauchi and Noriyoshi Sukegawa:

Redundant constraints in the standard formulation for the clique partitioning problem,

Optimization Letters 9, pp. 199–207, 2015.

• Tomomi Matsui, Noriyoshi Sukegawa, and Atsushi Miyauchi:

Fractional programming formulation for the vertex coloring problem,

Information Processing Letters 114, pp. 706–709, 2014.

• Noriyoshi Sukegawa and Atsushi Miyauchi:

A note on the complexity of the maximum edge clique partitioning problem with respect to the clique number,

Discrete Optimization 10, pp. 331–332, 2013.

• Atsushi Miyauchi and Yuichiro Miyamoto:

Computing an upper bound of modularity,

European Physical Journal B 86, 302 (7 pages), 2013.