Discovering K-Core-Truss on Multilayer Graphs

Multilayer graphs are an effective framework for mode <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ling}$</tex> complex systems and have garnered significant research attention. Previous studies on dense structure decomposition in multilayer graphs have generally relied on a single perspective to describe density. In contrast, this paper introduces a novel model called multilayer-k-core-truss, which combines the concepts of vertex importance as defined by both k-core and k-truss, thereby capturing relationships across multiple dimensions. Building on this dense subgraph model, we develop a basic decomposition algorithm and propose the MKCT-Index to efficiently address search problems. Extensive experiments on several real-world da-tasets validate the effectiveness and efficiency of our proposed algorithms.

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Published
2024-10-11
DOI
https://doi.org/10.1109/cbase64041.2024.10824252
Primary Topic
Advanced Graph Theory Research
Type
article
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article

Discovering K-Core-Truss on Multilayer Graphs

Licheng Zhuo
Advanced Graph Theory Research
article

Discovering K-Core-Truss on Multilayer Graphs

Licheng Zhuo
article en

Abstract

Multilayer graphs are an effective framework for mode <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ling}$</tex> complex systems and have garnered significant research attention. Previous studies on dense structure decomposition in multilayer graphs have generally relied on a single perspective to describe density. In contrast, this paper introduces a novel model called multilayer-k-core-truss, which combines the concepts of vertex importance as defined by both k-core and k-truss, thereby capturing relationships across multiple dimensions. Building on this dense subgraph model, we develop a basic decomposition algorithm and propose the MKCT-Index to efficiently address search problems. Extensive experiments on several real-world da-tasets validate the effectiveness and efficiency of our proposed algorithms.

Nanjing University of Science and Technology (CN)
Openalex Percentile: Top 70%
Advanced Graph Theory Research
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