Link-Prediction and its Application in Online Social Networks

Tang, Feiyi (2017) Link-Prediction and its Application in Online Social Networks. PhD thesis, Victoria University.

Abstract

Alongside the continuous development of Internet technologies, traditional social networks are running online to provide more services so as to unite the community. In the meantime, conventional web-based information systems are trying hard to utilise social networking elements to develop a virtual community so as to increase their popularity. The combination of these two domains has become what people knew as the ‘online social networks’. There is much to do to reveal the knowledge behind the screen as massive amounts of user-generated data is created every second. Many people from different disciplines are using their tools and techniques to analyse and build knowledge to try understanding the evolution of it. Link Prediction, with the essence of calculating similarities of two nodes, is one of the most common techniques to analyse an online social network. It is worth mentioning that while using Link Prediction to explain online social network, we consider it as a graph with nodes and edges connecting one another where nodes represent individuals and edges represent the relations between them. Link Prediction can be utilised in many ways in this domain, where one of the most common ways is predicting links/edges that may appear in the future of an evolving network where links/edges represent connections. The meaning of these connections vary under different circumstance, such as an academia social network where they may represent co-author relationships among researchers. Therefore, one of the most common applications of Link Prediction in an online social network will be the recommendation system. Many works have been done to analyse social-oriented online networks and many turns into applications with great success such as Facebook and Twitter. However, this thesis concentrates on investigating a particular type of online social network where there is still a large gap waiting to be filled - the online academia social network. The objective of this thesis is to provide a more sensible way for people to understand the evolution of this network and develop models and algorithms that solving issues in regards to the needs of the users in this system of finding valuable research partners. Further the object is to building up an environment for future researchers to share knowledge and to carry on the work as a community. To be specific, this thesis contains four main chapters, and they are connected in some ways to develop solutions for the issues coming out during the research processes.

Item type Thesis (PhD thesis)
URI https://vuir.vu.edu.au/id/eprint/35048
Subjects Current > FOR Classification > 0801 Artificial Intelligence and Image Processing
Current > Division/Research > College of Science and Engineering
Keywords OSNS, online social networking system, social media, user relationship analysis, friend recommendation, user-interest-tags, SCHOLAT, sparse ranking model adaptation, cross-domain learning
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