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  1. 論文誌(トランザクション)
  2. 数理モデル化と応用(TOM)
  3. Vol.17
  4. No.1

An Agent-based Model for Capturing Diverse Interactions in Social Networks

https://ipsj.ixsq.nii.ac.jp/records/232852
https://ipsj.ixsq.nii.ac.jp/records/232852
8058b06d-af4e-4a97-8e7c-356d52a9d7cc
名前 / ファイル ライセンス アクション
IPSJ-TOM1701003.pdf IPSJ-TOM1701003.pdf (4.4 MB)
 2026年2月28日からダウンロード可能です。
Copyright (c) 2024 by the Information Processing Society of Japan
非会員:¥660, IPSJ:学会員:¥330, MPS:会員:¥0, DLIB:会員:¥0
Item type Trans(1)
公開日 2024-02-28
タイトル
タイトル An Agent-based Model for Capturing Diverse Interactions in Social Networks
タイトル
言語 en
タイトル An Agent-based Model for Capturing Diverse Interactions in Social Networks
言語
言語 eng
キーワード
主題Scheme Other
主題 [オリジナル論文] social network, agent-based model, adjacent possible space, quality diversity
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者所属
University of Tsukuba
著者所属
University of Tsukuba
著者所属
University of Tsukuba
著者所属
University of Tsukuba/Blank Space Inc.
著者所属(英)
en
University of Tsukuba
著者所属(英)
en
University of Tsukuba
著者所属(英)
en
University of Tsukuba
著者所属(英)
en
University of Tsukuba / Blank Space Inc.
著者名 Nanami, Iwahashi

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Nanami, Iwahashi

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Junya, Okabe

× Junya, Okabe

Junya, Okabe

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Mikihiro, Suda

× Mikihiro, Suda

Mikihiro, Suda

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Mizuki, Oka

× Mizuki, Oka

Mizuki, Oka

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著者名(英) Nanami, Iwahashi

× Nanami, Iwahashi

en Nanami, Iwahashi

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Junya, Okabe

× Junya, Okabe

en Junya, Okabe

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Mikihiro, Suda

× Mikihiro, Suda

en Mikihiro, Suda

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Mizuki, Oka

× Mizuki, Oka

en Mizuki, Oka

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論文抄録
内容記述タイプ Other
内容記述 In this study, we delve into the intricacies of social network growth by scrutinizing interactions with existing contacts, the establishment of new connections, and the strategies utilized to foster these connections. Grounded in existing research, we introduce a novel agent-based model predicated on the well-established concept of the “adjacent possible space” to facilitate network growth. The essence of this model lies in the way it explores new connections from the adjacent possible space, an essential factor in describing network growth. Building upon conventional methodologies, we have enhanced our model's ability to encapsulate a wide spectrum of connection strategies. Specifically, we propose a new approach that, unlike traditional search methods which only consider the set of strategies provided by the experimenter, allows for a broader range of choices and transforms these strategies into vector representations. Although this approach significantly increases the search space, we tackle this challenge using evolutionary algorithms, which guide our search within the expanded space. This algorithm manipulates strategies, encoded as a four-dimensional vector fully describing the parameters required to build a network, thereby ensuring flexible and efficient exploration within an expansive search space. Notably, this approach demonstrates a performance that aligns with conventional methods utilizing a brute-force search, underscoring its effectiveness. Our findings elucidate the nuanced dynamics of social network growth, offering substantial implications for practical applications within online services, including but not limited to social networking and gaming platforms. In such contexts, the identification of effective users and their interaction strategies can drive service growth and engagement.
論文抄録(英)
内容記述タイプ Other
内容記述 In this study, we delve into the intricacies of social network growth by scrutinizing interactions with existing contacts, the establishment of new connections, and the strategies utilized to foster these connections. Grounded in existing research, we introduce a novel agent-based model predicated on the well-established concept of the “adjacent possible space” to facilitate network growth. The essence of this model lies in the way it explores new connections from the adjacent possible space, an essential factor in describing network growth. Building upon conventional methodologies, we have enhanced our model's ability to encapsulate a wide spectrum of connection strategies. Specifically, we propose a new approach that, unlike traditional search methods which only consider the set of strategies provided by the experimenter, allows for a broader range of choices and transforms these strategies into vector representations. Although this approach significantly increases the search space, we tackle this challenge using evolutionary algorithms, which guide our search within the expanded space. This algorithm manipulates strategies, encoded as a four-dimensional vector fully describing the parameters required to build a network, thereby ensuring flexible and efficient exploration within an expansive search space. Notably, this approach demonstrates a performance that aligns with conventional methods utilizing a brute-force search, underscoring its effectiveness. Our findings elucidate the nuanced dynamics of social network growth, offering substantial implications for practical applications within online services, including but not limited to social networking and gaming platforms. In such contexts, the identification of effective users and their interaction strategies can drive service growth and engagement.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA11464803
書誌情報 情報処理学会論文誌数理モデル化と応用(TOM)

巻 17, 号 1, p. 11-22, 発行日 2024-02-28
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-7780
出版者
言語 ja
出版者 情報処理学会
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