Experimental analysis and evaluation of cohesive subgraph discovery

Dahee Kim; Song Kim; Jeongseon Kim; Junghoon Kim; Kaiyu Feng; Sungsu Lim; Jungeun Kim

doi:10.1016/j.ins.2024.120664

Experimental analysis and evaluation of cohesive subgraph discovery

Dahee Kim
, Song Kim
, Jeongseon Kim
, Junghoon Kim^*
, Kaiyu Feng
, Sungsu Lim
, Jungeun Kim

^*此作品的通讯作者

计算机学院

Ulsan National Institute of Science and Technology
Chungnam National University
Kongju National University

科研成果: 期刊稿件 › 文章 › 同行评审

4 引用（Scopus）

摘要

Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.

源语言	英语
文章编号	120664
期刊	Information Sciences
卷	672
DOI	https://doi.org/10.1016/j.ins.2024.120664
出版状态	已出版 - 6月 2024

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10.1016/j.ins.2024.120664

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title = "Experimental analysis and evaluation of cohesive subgraph discovery",

abstract = "Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.",

keywords = "Cohesive subgraph discovery, Community detection, Social network analysis",

author = "Dahee Kim and Song Kim and Jeongseon Kim and Junghoon Kim and Kaiyu Feng and Sungsu Lim and Jungeun Kim",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier Inc.",

year = "2024",

month = jun,

doi = "10.1016/j.ins.2024.120664",

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TY - JOUR

T1 - Experimental analysis and evaluation of cohesive subgraph discovery

AU - Kim, Dahee

AU - Kim, Song

AU - Kim, Jeongseon

AU - Kim, Junghoon

AU - Feng, Kaiyu

AU - Lim, Sungsu

AU - Kim, Jungeun

PY - 2024/6

Y1 - 2024/6

N2 - Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.

AB - Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.

KW - Cohesive subgraph discovery

KW - Community detection

KW - Social network analysis

UR - https://www.scopus.com/pages/publications/85192081151

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DO - 10.1016/j.ins.2024.120664

M3 - Article

AN - SCOPUS:85192081151

SN - 0020-0255

VL - 672

JO - Information Sciences

JF - Information Sciences

M1 - 120664

ER -

Experimental analysis and evaluation of cohesive subgraph discovery

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