# Tracing Network Evolution Using The Parafac2 Model

@article{Roald2020TracingNE, title={Tracing Network Evolution Using The Parafac2 Model}, author={Marie Roald and Suchita Bhinge and Chunying Jia and Vince D. Calhoun and T. Adalı and Evrim Acar}, journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2020}, pages={1100-1104} }

Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood. A traditional approach in neuroimaging data analysis is to make simplifications through the assumption of static spatial networks. In this paper, without assuming static networks in time and/or space, we arrange the…

## 5 Citations

PARAFAC2 AO-ADMM: Constraints in all modes

- Computer Science2021 29th European Signal Processing Conference (EUSIPCO)
- 2021

The proposed alternating direction method of multipliers (ADMM)-based algorithm for fitting PARAFAC2 can accurately recover the underlying components from simulated data while being both computationally efficient and flexible in terms of imposing constraints.

LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values

- Computer ScienceKDD
- 2020

This paper proposes Logistic PARAFAC2 (LogPar) by modeling the binary irregular Tensor with Bernoulli distribution parameterized by an underlying real-valued tensor and approximate the underlying tensor with a positive-unlabeled learning loss function to account for the missing values.

Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches

- Computer ScienceFrontiers in Neuroscience
- 2022

This article arranges time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyzes the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics, showing that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured.

An AO-ADMM approach to constraining PARAFAC2 on all modes

- Computer ScienceArXiv
- 2021

An algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM) is proposed and it is shown that constraining the evolving mode improves the interpretability of the extracted patterns.

Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study

- Computer Science
- 2021

Numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

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