Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning. - Seiji Maekawa; Koh Takeuchi 001; Makoto Onizuka (CoRR, 2018)

Controlling Internal Structure of Communities on Graph Generator. - Hiroto Yamaguchi; Yuya Ogawa; Seiji Maekawa; Yuya Sasaki 001; Makoto Onizuka (ASONAM, 2020)

New Attributed Graph Clustering by Bridging Attribute and Topology Spaces. - Seiji Maekawa; Koh Takeuchi 001; Makoto Onizuka (J. Inf. Process., 2020)

Adaptive Node Embedding Propagation for Semi-supervised Classification. - Yuya Ogawa; Seiji Maekawa; Yuya Sasaki 001; Yasuhiro Fujiwara; Makoto Onizuka (ECML/PKDD (2), 2021)

Effective Candidate Selection and Interpretable Interest Extraction for Follower Prediction on Social Media. - Seiji Maekawa; Santi Saeyor; Takeshi Sakaki; Makoto Onizuka (WI/IAT, 2021)

Low-resource Interactive Active Labeling for Fine-tuning Language Models. - Seiji Maekawa; Dan Zhang 025; Hannah Kim 001; Sajjadur Rahman; Estevam Hruschka (EMNLP (Findings), 2022)

Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs. - Seiji Maekawa; Koki Noda; Yuya Sasaki 001; Makoto Onizuka (NeurIPS, 2022)

GNN Transformation Framework for Improving Efficiency and Scalability. - Seiji Maekawa; Yuya Sasaki 001; George Fletcher 001; Makoto Onizuka (ECML/PKDD (2), 2022)

Benchmarking GNNs with GenCAT Workbench. - Seiji Maekawa; Yuya Sasaki 001; George Fletcher 001; Makoto Onizuka (ECML/PKDD (6), 2022)

Why Using Either Aggregated Features or Adjacency Lists in Directed or Undirected Graph? Empirical Study and Simple Classification Method. - Seiji Maekawa; Yuya Sasaki 001; Makoto Onizuka (CoRR, 2023)

GenCAT: Generating attributed graphs with controlled relationships between classes, attributes, and topology. - Seiji Maekawa; Yuya Sasaki 001; George Fletcher 001; Makoto Onizuka (Inf. Syst., 2023)

Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models. - Seiji Maekawa; Hayate Iso; Sairam Gurajada; Nikita Bhutani (NAACL-HLT, 2024)

From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization. - Catarina G. Belém; Pouya Pezeskhpour; Hayate Iso; Seiji Maekawa; Nikita Bhutani; Estevam Hruschka (CoRR, 2024)

Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data. - Seiji Maekawa; Hayate Iso; Nikita Bhutani (ICLR, 2025)

From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization. - Catarina G. Belém; Pouya Pezeshkpour; Hayate Iso; Seiji Maekawa; Nikita Bhutani; Estevam Hruschka (NAACL (Findings), 2025)

Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k. - Chihiro Taguchi; Seiji Maekawa; Nikita Bhutani (CoRR, 2025)