Discovering hidden topical hubs and authorities in online social networks

• Main Authors: LEE, Roy Ka-Wei, HOANG, Tuan-Anh, LIM, Ee-Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: HITS algorithms; Link analysis; On-line social networks; Real-world datasets; State of the art; State-of-the-art methods; Topic model; Topic Modeling; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4081; https://ink.library.smu.edu.sg/context/sis_research/article/5084/viewcontent/Discovering_hidden_topical_hubs_afv_2018.pdf
• Institution: Singapore Management University

Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process so as to jointly learn the hub, authority and topical interests. We evaluate HAT against several existing state-of-the-art methods in two aspects: (i) modeling of topics, and (ii) link recommendation. We conduct experiments on two real-world datasets from Twitter and Instagram. Our experiment results show that HAT is comparable to state-of-the-art topic models in learning topics and it outperforms the state-of-the-art in link recommendation task.