- Andreas Roth, Thomas Liebig:
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks. LoG 2023: 35 - Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan:
The Second Learning on Graphs Conference: Preface. LoG 2023: i-xix - Tuo Xu, Lei Zou:
Rethinking Higher-Order Representation Learning With Graph Neural Networks. LoG 2023: 38 - Naganand Yadati, Tarun Kumar, Deepak Maurya, Balaraman Ravindran, Partha P. Talukdar:
HEAL: Unlocking the Potential of Learning on Hypergraphs Enriched With Attributes and Layers. LoG 2023: 34 - Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang:
Cycle Invariant Positional Encoding for Graph Representation Learning. LoG 2023: 4 - Jiale Yan, Hiroaki Ito, Ángel López García-Arias, Yasuyuki Okoshi, Hikari Otsuka, Kazushi Kawamura, Thiem Van Chu, Masato Motomura:
Multicoated and Folded Graph Neural Networks With Strong Lottery Tickets. LoG 2023: 11 - Shiqing Yu, Mathias Drton, Ali Shojaie:
Interaction Models and Generalized Score Matching for Compositional Data. LoG 2023: 20 - Zhiwei Zhen, Yuzhou Chen, Murat Kantarcioglu, Kangkook Jee, Yulia R. Gel:
United We Stand, Divided We Fall: Networks to Graph (N2G) Abstraction for Robust Graph Classification Under Graph Label Corruption. LoG 2023: 30 - Xiandong Zou, Xiangyu Zhao, Pietro Lio, Yiren Zhao:
Will More Expressive Graph Neural Networks Do Better on Generative Tasks? LoG 2023: 21 - Soledad Villar, Benjamin Chamberlain:
Learning on Graphs Conference, 27-30 November 2023, Virtual Event. Proceedings of Machine Learning Research 231, PMLR 2023 [contents]