@inproceedings{0f0d8870bcf34df793bb87158cd953a3,
title = "Identifying influential positively perceived users in co-purchase networks",
abstract = "Identifying influential users is one of the important problems in social network analysis with many useful applications ranging from marketing to epidemics and opinion diffusion (e.g., on political issues). In this paper, we focus on how to determine influential users by correlating different centrality measures with how the users are perceived by their community. Specifically, we work on the Amazon product purchasing data and build a 'co-purchase' network connecting users who purchased the same items. We then identify influential users based on different structural measures (closeness centrality, betweenness centrality, etc.) and analyze the relationship between these measures and the community's perception of the users. Our analysis shows several interesting observations. For example, we observed a high correlation between having a high betweenness centrality and being viewed positively by the community.",
keywords = "amazon metadata, correlation, influence, social network analysis",
author = "Aisha Al-Sadi and Mahmoud Al-Ayyoub",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th International Conference on Information and Communication Systems, ICICS 2018 ; Conference date: 03-04-2018 Through 05-04-2018",
year = "2018",
month = may,
day = "4",
doi = "10.1109/IACS.2018.8355445",
language = "English",
series = "2018 9th International Conference on Information and Communication Systems, ICICS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "78--83",
booktitle = "2018 9th International Conference on Information and Communication Systems, ICICS 2018",
address = "United States",
}