Relational data represent relationships between entities anywhere on the web (e.g. online social networks) or in the physical world (e.g. structure of the protein).
Graph neural networks are intimately related to partial differential equations governing information diffusion on graphs. Thinking of GNNs as PDEs leads to a new broad class of graph ML methods.
TL;DR: Have you even wondered what is so special about convolution? In this post, I derive the convolution from first principles and show that it naturally emerges from translational symmetry. During…
P. Heim, J. Ziegler, and S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), volume 417 of CEUR Workshop Proceedings, page 49--58. Aachen, (2008)
Y. Yang, C. Huang, L. Xia, and C. Li. Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, page 1434--1443. (2022)
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, page 4278–4284. AAAI Press, (Aug 10, 2019)
D. Yang, P. Rosso, B. Li, and P. Cudre-Mauroux. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 1162–1172. New York, NY, USA, Association for Computing Machinery, (2019)