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Sadeghian, Sadaf

University of British Columbia

Temporal graphs have emerged as a fundamental tool for modeling dynamic systems across diverse domains. However, existing research predominantly centers on pairwise interactions, while many real-world complex systems involve interactions among multiple entities. Hypergraphs allow edges to connect any number of vertices, enabling the representation of higher-order structures in data. Consequently, extracting and learning patterns of temporal, higher-order interactions is important in domains such as social network analysis, neuroscience, and finance. This thesis makes two contributions. First, we introduce CAT-WALK, which is a method for representation learning on temporal hypergraphs. Then, we illustrate the value of temporal hypergraph modeling and CAT-WALK in neuroscience applications. We introduce HYPERBRAIN, a framework for detecting abnormal co-activations in brain networks. CAT-WALK is an inductive method that uses a novel higher-order random walk to learn hyperedge representations. It uses a novel permutation-invariant pooling strategy in conjunction with a set-based anonymization process to hide the identity of hyperedges. Additionally, we present a straightforward, yet effective, neural network model for encoding hyperedges. Through extensive experiments, we demonstrate the efficacy of CAT-WALK in 1) predicting future hyperedges and 2) classifying nodes. The second part of this thesis demonstrates how we can encode brain networks as hypergraphs and use CAT-WALK for analyzing them. We introduce HYPERBRAIN, an anomaly detection framework for temporal hypergraph brain networks. HYPERBRAIN first represents fMRI time series data as temporal hypergraphs and subsequently uses CAT-WALK for hypergraph representation learning. Customizing both the temporal higher-order walk and the training approach for the analysis of brain networks, HYPERBRAIN can effectively learn the structural and temporal properties of these brain networks and identify anomalous hyperedges in the brain of individuals with disorders. We evaluate the performance of HYPERBRAIN in both synthetic and real-world settings for Attention Deficit Hyperactivity Disorder (ADHD), and Autism Spectrum Disorder.

Text

2024-04-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87781

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/