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Community detection in networks : methods and biomedical applications

University of British Columbia

Community detection is an emerging topic in modern network science. This thesis focuses on developing data-driven network generation and community extraction tools targeted for biomedical research, which is hardly studied in the literature, owing to the struggle of approaches to overcome resolution limitations without prior information. In the first part of this thesis, a novel community detection approach is proposed to detect unknown community structure from both binary and non-binary networks. The method overcomes the resolution limitation of current approaches, while no prior information regarding the community structure is available. In the later parts of the thesis, three common biomedical scenarios are identified where domain-dependent network community extraction frameworks are proposed to solve open research challenges. The first setting represents the scenario when no prior information regarding the community structure is available, and as an illustrative example, we consider the functional segmentation of brainstem from fMRI timecourses. We propose a framework to extract functional communities within the brainstem, based on a data-driven generation and clustering of the functional network, and a consensus based group model development approach. In the second scenario, at the presence of additional information, a domain-specific framework is proposed to incorporate prior information in the network community extraction pipeline. As an illustrative example we consider the parcellation of putaminal sub-regions, and propose a robust community extraction framework where a primary brain region is parcellated into functional sub-regions incorporating prior information regarding the number of communities, the connectivity differences both within and outside the primary network and a constraint on spatial contiguity. In the final setting community extraction from a large-scale dataset is considered, and a unified approach is proposed to combine network community detection to the deep-learning based framework to form deep-communities, and its potential to be used as a deep-clustering tool is illustrated on a chest X-ray based image retrieval study. We propose a framework that integrates a deep learning-based image-network generation approach and a weighted modularity based network-community detection technique to form similar image communities. A region-growing based community formation framework is then applied to extract similar images at the presence of a new image.

Text

2020-05-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74285

Electrical and Computer Engineering

University of British Columbia

UBCV

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