@inproceedings{a8913a75a08b4baf80f35abab961ff57,
title = "THINK: Temporal Hypergraph Hyperbolic Network",
abstract = "Network-based time series forecasting is a challenging task as it involves complex geometric properties, higher-order relations, and scale-free characteristics. Previous work has modeled network-based series as oversimplified graphs or has ignored the power law dynamics of real-world temporal and dynamic networks, which could yield suboptimal results. With the aim to address these issues, here we propose THINK, a novel framework based on hypergraph learning that captures the hyperbolic properties of time-evolving dynamic hypergraphs. We design an elegant hyperbolic distance-aware hypergraph attention mechanism to better capture informative internal structural features on the Poincar{\'e} ball. Through quantitative and conceptual analysis on seven tasks across temporal, and time-evolving dynamic hypergraphs, we demonstrate THINK's practicality in comparison to a variety of benchmarks spanning finance, health, and energy networks.",
keywords = "hyperbolic, hypergraphs, spatio-temporal forecasting",
author = "Shivam Agarwal and Ramit Sawhney and Megh Thakkar and Preslav Nakov and Jiawei Han and Tyler Derr",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Data Mining, ICDM 2022 ; Conference date: 28-11-2022 Through 01-12-2022",
year = "2022",
doi = "10.1109/ICDM54844.2022.00096",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "849--854",
editor = "Xingquan Zhu and Sanjay Ranka and Thai, {My T.} and Takashi Washio and Xindong Wu",
booktitle = "Proceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022",
address = "United States",
}