Temporal hypergraph modeling via inter-geometrical learning

Hyperbolic geometry has advanced learning representations on graphs with inherently complex geometrical and hierarchical characteristics. However, most real world networks innately comprise of higher-order relations, dynamic behavior, and scale-free temporal characteristics with varying degrees of hyperbolicity. Combating these gaps, we propose THRONE, a temporal, inter-geometrical interaction learning-based hypergraph convolution method to capitalize on the complex, time-varying, higher-order relations and the varying hyperbolicity of network structures. Further, we enhance the hypergraph convolution by applying attention infused with hyperbolic distance information among node and hyperedge representations. THRONE incorporates hyperbolic temporal convolution layers to encode scale-free spatio-temporal information and dynamic time-evolving network structures. We extend THRONE to hypergraph-level tasks by introducing THRONE-Pool, a novel hyperbolic hypergraph pooling method to encode higher-order scale-free networks. Through a series of quantitative and exploratory analyses on ten node-level and six network-level tasks across static, spatio-temporal, and time-evolving dynamic hypergraphs, we demonstrate THRONE's practical applicability in comparison to competitive baselines. We dissect THRONE's performance contributions on a variety of benchmarks and applications spanning finance, health, traffic, wind energy, and citation networks through ablations to highlight the effectiveness of each component. Through THRONE, we take a step forward in devising a data, task, hyperbolicity and network agnostic method for learning representations.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Agarwal, Shivam

Contributors dc:contributor

• Han, Jiawei
• Peng, Hao

• Graph Neural Networks
• Spatio-temporal Learning
• Machine Learning

Statement dc:rights

• Copyright 2024 Shivam Agarwal

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124326

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124326