2017/08/14 by Mohammed Abufouda, Abufouda, Mohammed, Katharina A. Zweig +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1708.04030
openalex publication_date 2017/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Online social networks (OSNs) have become the main medium for connecting\npeople, sharing knowledge and information, and for communication. The social\nconnections between people using these OSNs are formed as virtual links (e.g.,\nfriendship and following connections) that connect people. These links are the\nheart of today's OSNs as they facilitate all of the activities that the members\nof a social network can do. However, many of these networks suffer from noisy\nlinks, i.e., links that do not reflect a real relationship or links that have a\nlow intensity, that change the structure of the network and prevent accurate\nanalysis of these networks. Hence, a process for assessing and ranking the\nlinks in a social network is crucial in order to sustain a healthy and real\nnetwork. Here, we define link assessment as the process of identifying noisy\nand non-noisy links in a network. In this paper, we address the problem of link\nassessment and link ranking in social networks using external interaction\nnetworks. In addition to a friendship social network, additional exogenous\ninteraction networks are utilized to make the assessment process more\nmeaningful. We employed machine learning classifiers for assessing and ranking\nthe links in the social network of interest using the data from exogenous\ninteraction networks. The method was tested with two different datasets, each\ncontaining the social network of interest, with the ground truth, along with\nthe exogenous interaction networks. The results show that it is possible to\neffectively assess the links of a social network using only the structure of a\nsingle network of the exogenous interaction networks, and also using the\nstructure of the whole set of exogenous interaction networks. The experiments\nshowed that some classifiers do better than others regarding both link\nclassification and link ranking.\n