Multi-scale community detection using stability optimisation

Martelot, Erwan Le; Hankin, Chris

doi:10.1504/ijwbc.2013.054907

Cited by 28 publications

(30 citation statements)

References 29 publications

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“…Several criteria designed for multi-scale analysis have been presented in [6,7,8,9,10]. However no efficient method to uncover communities across scales was suggested.…”

Section: Introductionmentioning

confidence: 99%

“…The stability of a graph (or network) partition considers the graph as a Markov chain where each node represents a state and each edge a possible state transition. The use of stability as an optimisation criterion was investigated in [8] where random walks of various length on a network are used to enable multi-scale analysis. Let d be the degree vector giving for each node its degree (or strength for a weighted network) and let D = diag(d) be the corresponding diagonal matrix.…”

Section: Introductionmentioning

confidence: 99%

“…A small value of t favours small communities while a large value favours large communities. Following the method from [8] the stability for a walk of length t can be expressed similarly to the modularity expression from equation (1) as…”

Section: Introductionmentioning

confidence: 99%

“…where t returns the smallest integer greater than t and t returns the greatest integer smaller than t. This is particularly useful to investigate time values between 0 and 1 as studies from [19,8] show that the use of Markov time within this interval enables detecting fine partitions.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, stability optimisation [8] enables random walks of variable length thus exploiting the actual structure of the network similarly to an information flow. The correlation with Markov chains also provides mathematical foundations giving the scale parameter an actual meaning.…”

Section: Introductionmentioning

confidence: 99%

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Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks

Martelot

Hankin

2013

The Computer Journal

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Nowadays, networks are almost ubiquitous. In the past decade, community detection received an increasing interest as a way to uncover the structure of networks by grouping nodes into communities more densely connected internally than externally. Yet most of the effective methods available do not consider the potential levels of organisation, or scales, a network may encompass and are therefore limited. In this paper we present a method compatible with global and local criteria that enables fast multi-scale community detection on large networks. The method is derived in two algorithms, one for each type of criterion, and implemented with 6 known criteria. Uncovering communities at various scales is a computationally expensive task. Therefore this work puts a strong emphasis on the reduction of computational complexity. Some heuristics are introduced for speed-up purposes. Experiments demonstrate the efficiency and accuracy of our method with respect to each algorithm and criterion by testing them against large generated multi-scale networks. This study also offers a comparison between criteria and between the global and local approaches. In particular our results suggest that global criteria seem to be more robust to noise and thus more accurate than local criteria.

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“…Several criteria designed for multi-scale analysis have been presented in [6,7,8,9,10]. However no efficient method to uncover communities across scales was suggested.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks

Martelot

Hankin

2013

The Computer Journal

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The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks

Dimitriadis

Messaritaki

Jones

2021

Human Brain Mapping

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A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.

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