This page serves as a repository for papers reviewed by the Network Science Lab members and their accompanying presentation materials as part of the AI-CUK Joint Journal Club. This weekly seminar series is a collaboration between various labs within the Department of Artificial Intelligence at the Catholic University of Korea, beginning in Winter 2022. Participating labs in the Joint Journal Club include the Network Science Lab, the BRAIN Lab (led by Prof. Dong-Hwa Jeong), and the Industrial AI Lab (led by Prof. Jaeyeon Jang). This seminar series enables researchers in the department to gain insights and intuitions by experiencing and communicating with other researchers working in different fields.
Table of Contents
Summer 2023
Aug 16th, 2023
H.E.Lee, Review on " A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks", Bioinformatics, 2022
Jul 27th, 2023
H.B.Kim, Review on "Sequential Recommendation with Graph Neural Networks", SIGIR 2021
Jul 19th, 2023
H.E.Lee, Review on "Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021
Jul 5th, 2023
H.B.Kim, Review on "Neural Graph Collaborative Filtering", SIGIR 2019
Jun 28th, 2023
H.E.Lee, Review on "Graph embedding on biomedical networks: methods, applications and evaluations", Bioinformatics, 2020
Spring 2023
May 30th, 2023
J.H.Lee, Review on "GraphMAE: Self-Supervised Masked Graph Autoencoders", KDD 2022
May 23rd, 2023
S.T.Nguyen, Review on "Graph Neural Networks for Link Prediction with Subgraph Sketching", ICLR 2023
May 16th, 2023
V.T.Hoang, Review on " Representing Long-Range Context for Graph Neural Networks with Global Attention" NeurIPS 2021
May 2nd, 2023
J.H.Lee, Review on "Learning Conjoint Attentions for Graph Neural Nets", NeurIPS 2021
Apr 11th, 2023
V.T.Hoang, Review on "NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs", ICLR 2023
Mar 28th, 2023
S.T.Nguyen, Review on "Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs", NeurIPS 2022 (Part 2)
Mar 21st, 2023
S.T.Nguyen, Review on "Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs", NeurIPS 2022 (Part 1)
Mar 14th, 2023
V.T.Hoang, Review on "Heterogeneous Graph Attention Network", WWW 2019
Mar 7th, 2023
M.W.Choi, Review on "Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures", Appied Energy, 2023
Winter 2022
Feb 28th, 2023
S.T.Nguyen, Review on "How Attentive are Graph Attention Networks?", ICLR 2022
Fed 21st, 2023
V.T.Hoang, Review on "Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns", KDD 2021
Feb 14th, 2023
M.W.Choi, Review on "Short-term wind speed forecasting based on spatial-temporal graph transformer networks", Energy, 2021
Feb 7th, 2023
S.T.Nguyen, Review on “Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks”, KDD 2019
Jan 31st, 2023
V.T.Hoang, Review on "Universal Graph Transformer Self-Attention Networks", WWW 2022
Jan 10th, 2023
S.T.Nguyen, Review on "Do Transformers Really Perform Bad for Graph Representation?", NeurIPS 2021
Jan 3rd, 2023
V.T.Hoang, Review on "Global Self-Attention as a Replacement for Graph Convolution", KDD 2022
