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Automated Influence Maintenance in Social Networks: an Agent-based Approach

Journal Article


Abstract


  • IEEE Social influence modelling and maximization appear significant in various domains, such as e-business, marketing, and social computing. Most existing studies focus on how to maximize positive social impact to promote product adoptions based on static network snapshots. Such approaches can only increase influence in a social network in short-term, but cannot generate sustainable or long-term effects. In this research work, we study on how to maintain long-term influence in a social network and propose an agent-based influence maintenance model, which can select influential nodes based on the current status in dynamic social networks in multiple times. Within the context of our investigation, the experimental results indicate that multiple-time seed selection is capable of achieving more constant impact than that of one-shot selection. We claim that influence maintenance is crucial for supporting, enhancing and assisting long-term goals in business development. The proposed approach can automatically maintain long-lasting impact and achieve influence maintenance.

Publication Date


  • 2018

Citation


  • Li, W., Bai, Q., Zhang, M. & Nguyen, T. (2018). Automated Influence Maintenance in Social Networks: an Agent-based Approach. IEEE Transactions on Knowledge and Data Engineering, 1-14.

Scopus Eid


  • 2-s2.0-85052631290

Number Of Pages


  • 13

Start Page


  • 1

End Page


  • 14

Place Of Publication


  • United States

Abstract


  • IEEE Social influence modelling and maximization appear significant in various domains, such as e-business, marketing, and social computing. Most existing studies focus on how to maximize positive social impact to promote product adoptions based on static network snapshots. Such approaches can only increase influence in a social network in short-term, but cannot generate sustainable or long-term effects. In this research work, we study on how to maintain long-term influence in a social network and propose an agent-based influence maintenance model, which can select influential nodes based on the current status in dynamic social networks in multiple times. Within the context of our investigation, the experimental results indicate that multiple-time seed selection is capable of achieving more constant impact than that of one-shot selection. We claim that influence maintenance is crucial for supporting, enhancing and assisting long-term goals in business development. The proposed approach can automatically maintain long-lasting impact and achieve influence maintenance.

Publication Date


  • 2018

Citation


  • Li, W., Bai, Q., Zhang, M. & Nguyen, T. (2018). Automated Influence Maintenance in Social Networks: an Agent-based Approach. IEEE Transactions on Knowledge and Data Engineering, 1-14.

Scopus Eid


  • 2-s2.0-85052631290

Number Of Pages


  • 13

Start Page


  • 1

End Page


  • 14

Place Of Publication


  • United States