-Monday 13:30-15:10
-Thursday 13:30-15:10
Oct. 2(Mon) Masunaga, "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019)", https://drive.google.com/file/d/1lfYIOReXDXHe36znwMSyLEQMcDc7vGpb/view?usp=sharing
Oct. 2(Mon) Hosomi, "Vision GNN: An Image is Worth Graph of Nodes", https://drive.google.com/file/d/1FplDqvz80ncD0TnPdLHqu71kwQ4cb3Hc/view?usp=sharing
Oct.12(Thu), Murasaki, "UltraE: Ultrahyperbolic Knowledge Graph Embeddings" (KDD 2022), https://drive.google.com/file/d/1UJHUlIACZRWmNGmd1WN5cJK1KmHH62DK/view?usp=sharing
Oct.12(Thu), Suzuki, "EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs" (ACM SIGMOD 2023) , https://drive.google.com/file/d/1FI9zwOcDgzSRrYnYn2JjuTNZQi3umGHZ/view?usp=sharing
Oct.16(Mon), Sakata, "Confidence-Based Feature Imputation for Graphs with Partially Known Features" (ICLR 2023), https://drive.google.com/file/d/1tVEY5zYwVsoPbkroETWAj3iKgrYj8eiD/view?usp=sharing
Oct.19(Thu), Omar, "Advancing A* Pathfinding through Cluster-Based Graph Convolution", https://drive.google.com/file/d/1xrPJPW6iXR2QA7mVbt-npUELZgBG_yiT/view?usp=sharing
Oct. 23(Mon), Yang, "COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning" (KDD 2022) , https://drive.google.com/file/d/1U_YILAsOvw2VVOWTsUREI6JwFuD6AEIO/view?usp=sharing
Oct.26(Thu), Miyazaki, "S2GAE: Self-Supervised Graph Autoencoders Are Generalizable Learners with Graph Masking" (WSDM 2023), https://drive.google.com/file/d/1HBcPIFXbBEuPFmp45-41tjzElQ_69xp6/view?usp=sharing
Nov.2(Thu), Jin, "Predicting Popularity Trend in Social Media Networks with Multi layer Temporal Graph Neural Networks", https://drive.google.com/file/d/1MWnF-9j14zv0waY5YAEvig_oI5OsuJIB/view?usp=sharing
Nov. 9(Thu), Jodelet, "Masked Autoencoders are Efficient Class Incremental Learners" (ICCV 2023), https://drive.google.com/file/d/1YXfVCG5AGOzTmV7DoFCKCZs_uY2jwVmr/view?usp=sharing
Nov. 13(Mon), Hafizh, "Graph-based Fraud Detection", https://drive.google.com/file/d/1PktRO44R0q8eUiCEyMhV8K5OQ7LC9wbH/view?usp=sharing
Nov. 16(Thu), Hasegawa, "DEGNN: Dual Experts Graph Neural Network Handling both Edge and Node Feature Noise", https://drive.google.com/file/d/1cTZZ4aS5R4ibWdHiR2RIac2pFdPWHxAI/view?usp=sharing