@article{oai:ipsj.ixsq.nii.ac.jp:00232852,
 author = {Nanami, Iwahashi and Junya, Okabe and Mikihiro, Suda and Mizuki, Oka and Nanami, Iwahashi and Junya, Okabe and Mikihiro, Suda and Mizuki, Oka},
 issue = {1},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Feb},
 note = {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., 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.},
 pages = {11--22},
 title = {An Agent-based Model for Capturing Diverse Interactions in Social Networks},
 volume = {17},
 year = {2024}
}