Classification Model using Contrast Patterns. - Hiroyuki Morita; Mao Nishiguchi (ICEIS (1), 2013)

CAECP and CRPD: Classification by Aggregating Essential Contrast Patterns, and Contrast Ranked Path Diagrams. - Mao Nishiguchi; Hiroyuki Morita (J. Inf. Knowl. Manag., 2016)

What Influences People to Broaden Their Horizons? - Kota Kakiuchi; Mao Nishiguchi; Fujio Toriumi; Masanori Takano; Kazuya Wada; Ichiro Fukuda (WI, 2018)

Fraudulent user detection on rating networks based on expanded balance theory and GCNs. - Wataru Kudo; Mao Nishiguchi; Fujio Toriumi (ASONAM, 2019)

Domain-Invariant Latent Representation Discovers Roles. - Shumpei Kikuta; Fujio Toriumi; Mao Nishiguchi; Tomoki Fukuma; Takanori Nishida; Shohei Usui (COMPLEX NETWORKS (1), 2019)

Readable Contrast Mining Method for Heterogeneous Bipartite Networks with Class Label. - Mao Nishiguchi; Hiroyuki Morita; Yasuyuki Shirai; Yusuke Goto (PACIS, 2020)

Framework for role discovery using transfer learning. - Shumpei Kikuta; Fujio Toriumi; Mao Nishiguchi; Shu Liu; Tomoki Fukuma; Takanori Nishida; Shohei Usui (Appl. Netw. Sci., 2020)

GCNEXT: graph convolutional network with expanded balance theory for fraudulent user detection. - Wataru Kudo; Mao Nishiguchi; Fujio Toriumi (Soc. Netw. Anal. Min., 2020)

Multiple Role Discovery in Complex Networks. - Shu Liu; Fujio Toriumi; Mao Nishiguchi; Shohei Usui (COMPLEX NETWORKS, 2021)

Corrective Information Does Not Necessarily Curb Social Disruption. - Ryusuke Iizuka; Fujio Toriumi; Mao Nishiguchi; Masanori Takano; Mitsuo Yoshida 001 (CoRR, 2021)

SignedS2V: Structural Embedding Method for Signed Networks. - Shu Liu; Fujio Toriumi; Xin Zeng; Mao Nishiguchi; Kenta Nakai (COMPLEX NETWORKS (1), 2022)

A flexible framework for multiple-role discovery in real networks. - Shu Liu; Fujio Toriumi; Mao Nishiguchi; Shohei Usui (Appl. Netw. Sci., 2022)

Reducing sexual predation and victimization through warnings and awareness among high-risk users. - Masanori Takano; Mao Nishiguchi; Fujio Toriumi (J. Comput. Soc. Sci., 2025)