Research

Title: Nonparametric inference for network generative mechanisms via graph spectra

Place: IMI Auditorium(W1-D-413) and Live streaming with Zoom

Speaker: Andre Fujita (Division of Network AI Statistics – Medical Institute of Bioregulation – Kyushu University)

Attendance: 26(Students: 7; Staff: 18;Others: 1)

Summary of Lecture:
At this IMI Colloquium, the speaker gave a lecture entitled “Nonparametric inference for network generative mechanisms via graph spectra”. The lecture introduced a framework for inferring network generation mechanisms within the context of complex systems by representing networks as random graphs and analyzing the graph spectra of their adjacency matrices. Specifically, the presentation covered topics such as parameter estimation for spectral distributions, model selection via the AIC, hypothesis testing for comparing multiple networks, the examination of correlations among networks, causal analysis, and clustering; examples involving brain networks were also presented. This lecture, which demonstrated how topics from mathematical fields such as eigenvalues, information theory, and statistical methods clarify complex structures, was highly valuable for faculty members and students in mathematics. A lively Q&A session continued even after the colloquium concluded, reflecting the participants’ keen interest in the lecture’s content.