A LexDFS-Based Approach on Finding Compact Communities

Creusefond, Jean; Largillier, Thomas; Peyronnet, Sylvain

doi:10.1007/978-3-319-51367-6_7

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…Therefore, it can be used to find minimum colorings as well as all minimal separators and all maximal cliques on this graph class [24]. Besides this, LexDFS was used in the field of data mining to design an efficient hierarchical clustering algorithm [9]. However, no general linear time implementation of LexDFS is known to date.…”

Section: Introductionmentioning

confidence: 99%

Linear Time LexDFS on Chordal Graphs

Beisegel,

Köhler,

Scheffler

et al. 2020

Preprint

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Lexicographic Depth First Search (LexDFS) is a special variant of a Depth First Search (DFS), which was introduced by Corneil and Krueger in 2008. While this search has been used in various applications, in contrast to other graph searches, no general linear time implementation is known to date. In 2014, Köhler and Mouatadid achieved linear running time to compute some special LexDFS orders for cocomparability graphs. In this paper, we present a linear time implementation of LexDFS for chordal graphs. Our algorithm is able to find any LexDFS order for this graph class. To the best of our knowledge this is the first unrestricted linear time implementation of LexDFS on a non-trivial graph class. In the algorithm we use a search tree computed by Lexicographic Breadth First Search (LexBFS).

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Section: Introductionmentioning

confidence: 99%

Linear Time LexDFS on Chordal Graphs

Beisegel,

Köhler,

Scheffler

et al. 2020

Preprint

View full text Add to dashboard Cite

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Biomolecular Clusters Identification in Linear Time Complexity for Biological Networks

Debnath

Rakshit²,

Sengupta³

et al. 2020

Advances in Intelligent Systems and Computing

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Fast detection of community structures using graph traversal in social networks

Basuchowdhuri¹,

Sikdar

Nagarajan³

et al. 2018

Knowl Inf Syst

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Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real-time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in O(|V | + |E|) time to create an initial cover before using modularity maximization to get the final cover.

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A LexDFS-Based Approach on Finding Compact Communities

Cited by 5 publications

References 35 publications

Linear Time LexDFS on Chordal Graphs

Linear Time LexDFS on Chordal Graphs

Biomolecular Clusters Identification in Linear Time Complexity for Biological Networks

Fast detection of community structures using graph traversal in social networks

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