AgentSphere: A Novel Influencer Maximization Algorithm using Agent-Based Modeling in Social Networks
AgentSphere: A Novel Influencer Maximization Algorithm using Agent-Based Modeling in Social Networks |
||
![]() |
![]() |
|
© 2024 by IJETT Journal | ||
Volume-72 Issue-7 |
||
Year of Publication : 2024 | ||
Author : Jyoti Sunil More, Vaishali V. Sarbhukan (Bodade), Yogesh Jadhav |
||
DOI : 10.14445/22315381/IJETT-V72I7P134 |
How to Cite?
Jyoti Sunil More, Vaishali V. Sarbhukan (Bodade), Yogesh Jadhav, "AgentSphere: A Novel Influencer Maximization Algorithm using Agent-Based Modeling in Social Networks," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 312-318, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P134
Abstract
Finding and using influencers to gain benefits is essential for marketing campaigns and information distribution in the field of social network analysis. In order to model and comprehend the intricate dynamics of social networks, this study presents AgentSphere, an inventive Influencer Maximisation Algorithm that makes use of Agent-Based Modeling (ABM). Each agent in the model represents a real-world user, capturing their various traits, actions, and capacities for impact. AgentSphere provides a realistic and nuanced depiction of social relationships through dynamic interactions and information dispersion, which helps with influencer optimisation and correct identification. The creation, application, and assessment of AgentSphere are presented in this research study, demonstrating its efficacy above conventional methods. The outcomes show how flexible AgentSphere is to the ever-changing social network landscape, making it an invaluable resource for influencer maximization in various domains.
Keywords
Agent based modeling, Social networks, Social media, Influencer maximization.
References
[1] Manuel Gomez Rodriguez, and Bernhard Schölkopf, “Influence Maximization in Continuous Time Diffusion Networks,” Proceedings of the 29th International Conference on Machine Learning (ICML), pp. 1661-1669, 2012.
[CrossRef] [Publisher Link]
[2] Hema Yoganarasimhan, “Impact of Social Network Structure on Content Propagation: A Study Using Youtube Data,” Quantitative Marketing and Economics, vol. 10, pp. 111-150, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Zongchao Li, and Cong Li, “Tweet or “Re-Tweet”? An Experiment of Message Strategy and Interactivity on Twitter,” Internet Research, vol. 24, no. 5, pp. 648-667, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Giannis Haralabopoulos, Ioannis Anagnostopoulos, and Sherali Zeadally, "Viral Content Propagation in Online Social Networks,” arXiv, pp. 1-12, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Eytan Bakshy et al., “The Role of Social Networks in Information Diffusion,” Proceedings of the 21st International Conference on World Wide Web, pp. 519-528, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Fuji Ren, and Ye Wu, “Predicting User-Topic Opinions in Twitter with Social and Topical Context,” IEEE Transactions on Affective Computing, vol. 4, no. 4, pp. 412-424, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiaoyan Qiu et al., “Limited Individual Attention and Online Virality of Low-Quality Information,” Nature Human Behaviour, vol. 1, no. 7, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Lilian Weng et al., “The Role of Information Diffusion in the Evolution of Social Networks,” Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 356-364, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Rumi Ghosh et al., “Time-Aware Ranking in Dynamic Citation Networks,” 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada, pp. 373-380, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Kristina Lerman, and Rumi Ghosh, “Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter,” Proceedings of the Eighth International Conference on Social Computing (ICSC), vol. 4, no. 1, pp. 90-97, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Meeyoung Cha et al., “Measuring User Influence in Twitter: The Million Follower Fallacy,” Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, vol. 4, pp. 10-17, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Eytan Bakshy et al., “Everyone's an Influencer: Quantifying Influence on Twitter,” Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65-74, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Cristian Danescu-Niculescu-Mizil et al., “No Country for Old Members: User Lifecycle and Linguistic Change in Online Communities,” Proceedings of the 22nd ACM International Conference on World Wide Web, pp. 307-318, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Robyn Caplan et al., “Algorithmic Accountability: A Primer,” Data & Society, pp. 1-28, 2018.
[Google Scholar] [Publisher Link]
[15] Nahla Mohamed Ahmed Ibrahim, and Ling Chen, “Link Prediction in Dynamic Social Networks by Integrating Different Types of Information,” Applied Intelligence, vol. 42, pp. 738-750, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Alessandro Acquisti, and Ralph Gross, “Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook,” Privacy Enhancing Technologies, Lecture Notes in Computer Science, vol. 4258, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jure Leskovec et al., “Cost-Effective Outbreak Detection in Networks,” Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420-429, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Yi Yang et al., “Cross-Platform Comparison of Framed Topics in Twitter and Weibo: Machine Learning Approaches to Social Media Text Mining,” Social Network Analysis and Mining, vol. 11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] David Matthias Kempe, Jon Michael Kleinberg, and É Tardos, “Maximizing the Spread of Influence Through a Social Network,” Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137-146, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Arvind Narayanan, and Vitaly Shmatikov, “De-Anonymizing Social Networks,” 2009 30th IEEE Symposium on Security and Privacy, Oakland, CA, USA, pp. 173-187, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Haiqian Gu et al., “Cross-Platform Modeling of Users' Behavior on Social Media,” 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, pp. 183-190, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Maskim Kitsak et al., “Identification of Influential Spreaders in Complex Networks,” Nature Physics, vol. 6, pp. 888-893, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sancheng Peng et al., “Influence Analysis in Social Networks: A Survey,” Journal of Network and Computer Applications, vol. 106, pp. 17-32, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Jure Leskovec, and Eric J. Horvitz, “Planetary-Scale Views on a Large Instant-Messaging Network,” Proceedings of the 17th ACM International Conference on World Wide Web, pp. 915-924, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Martin Neumann, “Emergence as an Explanatory Principle in Artificial Societies. Reflection on the Bottom-Up Approach to Social Theory,” Epistemological Aspects of Computer Simulation in the Social Sciences, Lecture Notes in Computer Science, vol. 5466, pp. 69-88, 2009.
[Google Scholar] [Publisher Link]
[26] Sandra González-Bailón et al., “The Dynamics of Protest Recruitment through an Online Network,” Scientific Reports, vol. 1, pp. 1-17, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Andreas Flache, Michael Mäs, and Marijn A. Keijzer, Chapter4: Computational Approaches in Rigorous Sociology: Agent-based Computational Modeling and Computational Social Science, Handbook of Sociological Science, Edward Elgar Publishing, pp. 57-72, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Federico Bianchi, and Flaminio Squazzoni, “Agent‐Based Models in Sociology,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 7, no. 4, pp. 284-306, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Zhanli Sun et al., “Simple or Complicated Agent-Based Models? A Complicated Issue,” Environmental Modelling & Software, vol. 86, pp. 56-67, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Damon Centola, “The Spread of Behavior in an Online Social Network Experiment,” Science, vol. 329, no. 5996, pp. 1194-1197, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Nigel Gilbert, and Klaus G. Troitzsch, Simulation for the Social Scientist, 2nd ed., McGraw-Hill Education, pp. 1-295, 2005.
[Google Scholar] [Publisher Link]
[32] Giovanna Miritello et al., “Limited Communication Capacity Unveils Strategies for Human Interaction,” Scientific Reports, vol. 3, pp. 1-7, 2013.
[CrossRef] [Google Scholar] [Publisher Link]