Network community detection and clustering with random walks

Ballal, Aditya; Kion-Crosby, Willow; Morozov, Alexandre V.

doi:10.1103/physrevresearch.4.043117

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…The walk-likelihood algorithm (WLA) is a graph partitioning algorithm first introduced by Ballal, Kion-Crosby, and Morozov [ 20 ]. The algorithm takes the transition matrix of a graph, the connectivity of each node in the graph, and an initial guess, a partition of the graph into m communities.…”

Section: Methodsmentioning

confidence: 99%

“…The walk-likelihood community finder (WLCF) is a community detection algorithm introduced in [ 20 ] that exploits the random walk properties of graphs to detect communities. Unlike WLA, WLCF automatically detects the number of communities within a graph based on the modularity, a quantitative metric that evaluates the degree of community structure in a network [ 21 ], of the graph.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we present MapperPlus, a novel unsupervised clustering pipeline which addresses this need. MapperPlus builds on Mapper’s topological graph and blends it with two random-walk techniques [ 20 ] for clustering on a graph network. MapperPlus automatically detects the number of clusters within a dataset and then generates a disjoint clustering of the data.…”

Section: Introductionmentioning

confidence: 99%

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MapperPlus: Agnostic clustering of high-dimension data for precision medicine

Datta,

Ballal,

López

et al. 2023

PLOS Digit Health

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One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms traditional agnostic clustering methods in key accuracy/performance metrics by testing its performance on publicly available medical and non-medical data set. We also demonstrate the predictive power of MapperPlus in a medical dataset of pediatric stem cell transplant patients where a number of cluster is unknown. Here, MapperPlus stratifies the patient population into clusters with distinctive survival rates. The MapperPlus software is open-source and publicly available.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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