In the last decade, online social networks have become an integral part of life. These networks play an important role in the dissemination of news, individual communication, disclosure of information, and business operations. Understanding the structure and implications of these networks is of great interest to both academia and industry. However, the unstructured nature of the graphs and the complexity of existing network analysis methods limit the effective analysis of these networks, particularly on a large scale. In this research, we propose a simple but effective node embedding method for the analysis of graphs with a focus on its application in online social networks. Our proposed method not only quantifies social graphs in a structured format but also enables user preference identification, community detection, and link prediction in online social networks. We demonstrate the effectiveness of our approach using a network of Twitter users. The results of this research provide valuable insights for marketing professionals seeking to target personalized content and advertising to individual users as well as social network administrators seeking to improve their platform through recommendation systems and the detection of outliers and anomalies.
Sharif Vaghefi, Mahyar and Nazareth, Derek L.
"Mining Online Social Networks: Deriving User Preferences through Node Embedding,"
Journal of the Association for Information Systems, 22(6), 1625-1658.
Available at: https://aisel.aisnet.org/jais/vol22/iss6/6
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