AISTATS 2022poster1 citations
Orthogonal Multi-Manifold Enriching of Directed Networks
Ramit Sawhney, Shivam Agarwal, Atula T. Neerkaje, Kapil Jayesh Pathak
Abstract
Directed Acyclic Graphs and trees are widely prevalent in several real-world applications. These hierarchical structures show intriguing properties such as scale-free and bipartite nature, with fine-grained temporal irregularities among nodes. Building on advances in geometrical deep learning, we explore a time-aware neural network to model trees and Directed Acyclic Graphs in multiple Riemannian manifolds of varying curvatures. To jointly utilize the strength of these manifolds, we propose
BibTeX
@InProceedings{pmlr-v151-sawhney22a,
title = { Orthogonal Multi-Manifold Enriching of Directed Networks },
author = {Sawhney, Ramit and Agarwal, Shivam and Neerkaje, Atula T. and Jayesh Pathak, Kapil},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {6074--6086},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/sawhney22a/sawhney22a.pdf},
url = {https://proceedings.mlr.press/v151/sawhney22a.html},
abstract = { Directed Acyclic Graphs and trees are widely prevalent in several real-world applications. These hierarchical structures show intriguing properties such as scale-free and bipartite nature, with fine-grained temporal irregularities among nodes. Building on advances in geometrical deep learning, we explore a time-aware neural network to model trees and Directed Acyclic Graphs in multiple Riemannian manifolds of varying curvatures. To jointly utilize the strength of these manifolds, we propose