그래프 신경망 (GNN: graph neural network)은 그래프에서 높은 표현 능력과 함께 특징 정보를 추출하는 방법론으로 학계와 산업체에서 최근 폭발적인 관심을 받고 있다. 본 세미나에서는 그래프 신경망의 개요 및 주요 동작 원리를 다룬다. 구체적으로, message passing의 원리를 이해하고 state-of-the-art 알고리즘에서 사용한 다양한 message passing 함수를 소개한다. 그래프 신경망을 활용한 다양한 downstream 응용 문제들이 존재하지만, 본 세미나에서는 근본적인 그래프 마이닝 문제 중 하나인 네트워크 정렬 (network alignment)으로의 적용을 다룬다. 네트워크 정렬 문제를 정의하고, 기존 연구 결과물들을 요약하고 한계점에 대해 설명한다. 이를 바탕으로 발표자 연구실에서 제안한 그래프 신경망을 활용한 새로운 점진적 네트워크 정렬 방법을 소개한다. 마지막으로, 그래프 신경망을 사용해 해결할 수 있는 다양한 실세계 응용 문제를 공유하고 토의한다.

We present new (mostly determinantal) expressions for various eigenvalue statistics in random matrix theory. Whenever the eigenvalue $n$-point correlation function is given in terms of $n \times n$ determinants with some kernel, we propose a new kernel that gives the $n$-point correlation function of the eigenvalues conditioned on the event of some eigenvalues already existing at fixed positions. Using such new kernels we obtain determinantal expressions for the joint densities of the $k$ largest eigenvalues, probability density function of the $k$-th largest eigenvalue, density of the first eigenvalue spacing, and many more. Our formulae is highly amenable to numerical computation through the method proposed by Bornemann (2008).

Since conformal field theory (CFT) was introduced to relate various critical statistical physics models to Virasoro representation theory, it has been applied to string theory, condensed matter physics, vertex operator algebra, and probability theory. In this talk, I will explain how the boundary CFT of central charges c (less than or equal to 1) relates to the boundary CFT of 26 - c in terms of boundary conditions. (On the algebraic side, Feigin and Fuchs described the duality between the categories of Verma modules with central charges c and 26 - c to explain the appearance of the critical dimension 26 of the bosonic string theory.) I will present the connection between the boundary CFT and the theory of Schramm-Loewner evolution in various conformal types.

TBA

In this talk, we derive effective dynamics governed by PDEs (nonlinear Schrodinger equation, Vlasov-Poisson equation) from the N-body Schrödinger equation with interactions in the large N limit. For the derivation, we first visit quantum mechanics. Then, using the knowledge of many-body quantum mechanics given in the talk and with the suitable conditions for each situation, we derive PDEs describing the effective motion of the system.