Schedule of the Seminar:

Introduction to Graph Neural Networks (Jadwiga Świerczyńska, October 14, 2024)

Graph Neural Networks for Node Classification (Mateusz Biłyk, October 14, 2024)

The Expressive Power of Graph Neural Networks (Mikołaj Hasik, October 21, 2024)

Graph Neural Networks: Graph Classification (Denys Tsebulia, October 28, 2024)

Graph Neural Networks: Link Prediction (Jakub Skalski, October 28, 2024)

Graph Neural Networks: Graph Transformation (Mafalda Bastos da Costa, November 4, 2024)

GNN-based Biomedical Knowledge Graph Mining in Drug Development (Maksymilian Perduta, November 4, 2024)

Graph Neural Networks: Graph Generation (Mariana Costa Osiecka de Carvalho, November 18, 2024)

GNNExplainer and Interpretability in Graph Neural Networks (Łukasz Halada, November 18, 2024)

Graph Neural Networks in Urban Intelligence (Szymon Kiczak, November 25, 2024)

Dynamic Graph Neural Networks (Karol Ochman-Milarski, November 25, 2024)

Temporal Graph Networks for Deep Learning on Dynamic Graphs (Mikołaj Hasik, December 2, 2024) [PDF]

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (Jakub Skalski, December 2, 2024) [PDF]

Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data (Łukasz Halada, December 16, 2024) [PDF]

Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction (Mafalda Bastos da Costa, January 13, 2025) [PDF]

Variational Graph Recurrent Neural Networks (Mariana Costa Osiecka de Carvalho, January 13, 2025) [PDF]

2nd Round of Papers:

Semi-Supervised Classification with Graph Convolutional Networks [PDF]

Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision [PDF]

Graph Random Neural Network for Semi-Supervised Learning on Graphs [PDF]

E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification [PDF]

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (selected topics) [PDF]

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (selected topics) [PDF]

Gated Graph Sequence Neural Networks [PDF]

Link Prediction Based on Graph Neural Networks [PDF]

Bayesian Flow Networks [PDF]

Bayesian Graph Neural Networks with Adaptive Connection Sampling [PDF]

Bayesian Graph Convolutional Neural Networks using Node Copying [PDF]

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification [PDF]

Dynamic Graph Representation Learning via Self-Attention Networks [PDF]

Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective [PDF]

Temporal Graph Neural Networks for Irregular Data [PDF]

Temporal Graph Networks for Deep Learning on Dynamic Graphs [PDF]

Additional Materials:

Understanding Variational Autoencoders [LINK]