It is estimated that there are now 3.48 billion users of Online Social Networks (OSNs) in 2019, engaging in a plethora of social and professional interactions. These interactions provide researchers with underlying data that can be analysed to gain useful insights about individuals, communities and societies.
In this video, Professor Lim Ee Peng shares highlights from his research and how he develops models and algorithms that can help us better understand user behaviour, and the research and application opportunities.
Lim Ee Peng is a Professor of Information Systems and the Director of the Living Analytics Research Centre (LARC).
Data Mining & Analytics, Social Media, Content Mining and Analysis, Social Network Mining and Analysis, Digital Libraries, Security Informatics, Data Integration
Xie; Lee, Ka Wei, Roy; Lee, Roy Ka-Wei; Zhu, Feida; And Lim, Ee-Peng. (2018). Unsupervised user identity linkage via factoid embedding. (2018). IEEE International Conference on Data Mining (ICDM2018), Singapore, 2018, November 17. Research Collection School Of Information Systems.
Abstract: User identity linkage (UIL), the problem of matching user account across multiple online social networks (OSNs), is widely studied and important to many real-world applications. Most existing UIL solutions adopt a supervised or semisupervised approach which generally suffer from scarcity of labeled data. In this paper, we propose Factoid Embedding, a novel framework that adopts an unsupervised approach. It is designed to cope with different profile attributes, content types and network links of different OSNs. The key idea is that each piece of information about a user identity describes the real identity owner, and thus distinguishes the owner from other users. We represent such a piece of information by a factoid and model it as a triplet consisting of user identity, predicate, and an object or another user identity. By embedding these factoids, we learn the user identity latent representations and link two user identities from different OSNs if they are close to each other in the user embedding space. Our Factoid Embedding algorithm is designed such that as we learn the embedding space, each embedded factoid is “translated” into a motion in the user embedding space to bring similar user identities closer, and different user identities further apart. Extensive experiments are conducted to evaluate Factoid Embedding on two real-world OSNs data sets. The experiment results show that Factoid Embedding outperforms the state-of-the-art methods even without training data.
Lee; Hee, Ming Shan; Prasetyo, Philips Kokoh; And Lim, Ee-Peng. (2018). LINKY: Visualizing user identity linkage results for multiple online social networks (Demo). IEEE International Conference on Data Mining (ICDM2018). Research Collection School Of Information Systems
Lee, Roy Ka-Wei; Hoang, Tuan-Anh; and Lim, Ee-peng. Discovering hidden topical hubs and authorities in online social networks. (2018). Proceedings of the SIAM International Conference on Data Mining (SDM18): San Diego, CA, May 3-5. 378-386.
Abstract: 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 realworld 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.
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