Network science is a field of study that aims to reveal structural patterns in real-world interaction networks, such as social networks, bibliographic networks, power grids, and others. The Network Science Lab (NS Lab; 네트워크과학연구실), part of the Dept. of AI at CUK, conducts extensive research on a wide range of theories, methodologies, and applications related to collection, representation, and analysis of networked data.
The Network Science Lab at the Catholic University of Korea releases a Causality Inference Framework, namely CaMol, a novel causality inference framework for few-shot molecular property prediction.
The Network Science Lab at the Catholic University of Korea releases Multi-View Conditional Information Bottleneck, namely MVCIB, a novel pre-trained Graph Neural Network model specialised in 2D and 3D Molecular Structures.
We are pleased to announce the establishment of a new Major of AI-Chemical Engineering in the Department of Artificial Intelligence at the Graduate School, in addition to the existing Major of Artificial Intelligence.
Members of the Network Science Lab at the Catholic University of Korea submitted one paper on mitigating the degree bias problem in the message passing mechanism to the IEEE TNSE (Transactions on Network Science and Engineering) and was accepted.
Van Thuy Hoang, a doctoral student at the Network Science Lab @ CUK, received the Best Award (최우수상) at the 22nd Graduate School Academic Awards (제22회 대학원 학술상) Ceremony for his outstanding research in graph learning, highlighting the lab's leadership in graph ML.