Simon Geisler, Yujia Li, Daniel Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru: Transformers Meet Directed Graphs.[Link]

Alex Morehead and Jianlin Cheng: Geometry-Complete Perceptron Networks for 3D Molecular Graphs.[Link]

Farhad Mohsin, Inwon Kang, Yuxuan Chen, Jingbo Shang and Lirong Xia: Dependency and Coreference-boosted Multi-Sentence Preference model.[Link]

Mert Kosan, Arlei Silva, Sourav Medya, Brian Uzzi and Ambuj Singh: Graph Macro Dynamics with Self-Attention for Event Detection.[Link]

Alessio Gravina, Davide Bacciu and Claudio Gallicchio: Non-dissipative propagation by anti-symmetric deep graph networks.[Link]

Benjamin Hilprecht, Kristian Kersting and Carsten Binnig: SPARE: A Single-Pass Neural Model for Relational Databases.[Link]

Wenjie Xi, Arnav Jain, Li Zhang and Jessica Lin: LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification.[Link]

Tiehua Zhang, Yuze Liu, Yao Yao, Youhua Xia, Xin Chen, Xiaowei Huang and Jiong Jin: Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks.[Link]

Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher Manning, Percy Liang and Jure Leskovec: Deep Bidirectional Language-Knowledge Graph Pretraining.[Link]

Jiamin Wu, Wenqi Zeng, Song Liu and Yuan Yao: SurfBind: Surface Distance aided Geometric Deep Learning for Binding Conformations.[Link]

Yeskendir Koishekenov: Reducing Over-smoothing in Graph Neural Networks using Relational Embeddings.[Link]

Atia Hamidizadeh, Tony Shen and Martin Ester: Semi-Supervised Junction Tree Variational Autoencoder for Molecular Graphs.[Link]