Link Prediction in Social Networks: A Dual Perspective on Positive and Negative Links

• Published in: 2024 Asian Conference on Intelligent Technologies (ACOIT) pp. 1 - 7
• Main Authors: Balvir, Sachin U., Raghuwanshi, Mukesh M., Shobhane, Purushottam D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.09.2024
• Subjects: Biological system modeling; Computational modeling; Correlation; Link prediction; Natural language processing; Negative link prediction; Positive link prediction; Predictive models; Reinforcement learning; Reviews; Security; Social Network Analysis; Social networking (online); Social networks; Social sciences
• ISBN: 9798350374933
• DOI: 10.1109/ACOIT62457.2024.10939432

Social networks encompass a variety of relationships, from positive relationships such as friendships to negative associations such as opposition. Link prediction is very important in these networks, extending its usefulness beyond social sciences to information retrieval, bioinformatics, e-commerce, etc. This paper presents various aspects on link prediction and gives them ways and means of doing good and negative aspects of relationships. Different methods proposed by researchers deal with correlation prediction differently, some involving only positive correlations, some negative correlations, and some addressing both areas simultaneously. Through these methods by examining it, this paper aims to provide insights into the development of network forecasting and the importance of socially meaningful network dynamics at work. This research gives the details overview of link prediction with both positive and negative aspects.