Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case

• URL: http://arxiv.org/abs/2002.09943v1
• Date: Tue, 18 Feb 2020 19:48:38 GMT
• Title: Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
• Authors: Cong Ye, Konstantinos Slavakis, Pratik V. Patil, Johan Nakuci, Sarah F. Muldoon, John Medaglia
• Abstract summary: This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: state clustering, node clustering within states, and even subnetwork-state-sequence identification/tracking.
• Score: 6.78543866474958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or community detection), and even subnetwork-state-sequence identification/tracking. Via a bottom-up approach, features are first extracted from the raw nodal time-series data by kernel autoregressive-moving-average modeling to reveal non-linear dependencies and low-rank representations, and then mapped onto the Grassmann manifold (Grassmannian). All clustering tasks are performed by leveraging the underlying Riemannian geometry of the Grassmannian in a novel way. To validate the proposed framework, brain-network clustering is considered, where extensive numerical tests on synthetic and real functional magnetic resonance imaging (fMRI) data demonstrate that the advocated learning framework compares favorably versus several state-of-the-art clustering schemes.

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