Community discovery for academic papers using both citation and attribute information = 인용 정보와 속성 정보를 고려한 학술 논문 커뮤니티 발견

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The main goal of this thesis is to discover the high-quality communities for academic papers. Since tremendous amounts of academic papers have been published, many researchers experience diculties in exactly finding the papers in which they are interested. Community detection (or cluster discovery) can facilitate this task since it finds similar or relevant papers. Thus, community detection from academic papers has received a lot of attention, but it is still a challenging issue. Along this direction, citation analysis and attribute (e.g., content) analysis have been widely used. However, most existing methods focus on either citation analysis or attribute analysis, disregarding the other side. The novelty of this thesis is a complete merger between citation analysis and attribute analysis. Our approach constructs a network of academic papers by considering both types of information together and then performs clustering to obtain the communities of papers. In the network, an edge between two papers is created by considering both (i) the existence and importance of citations from one to the other and (ii) the attribute similarity between the two papers. In this way, the two types of information are considered at the same time, not sequentially. The optimal merger was empirically determined. Last, the effectiveness of our approach was verified by extensive experiments. About half-million papers were crawled, and the full text was extracted from them for attribute analysis. The results show that our approach produces higher-quality communities compared with the baseline approaches that use either citation analysis or attribute analysis. Overall, we believe that our approach will be very useful for academic search engines.
Lee, Jae-Gilresearcher이재길
한국과학기술원 : 지식서비스공학과,
Issue Date
515187/325007  / 020113148

학위논문(석사) - 한국과학기술원 : 지식서비스공학과, 2013.2, [ v, 36 p. ]


community; discovery; paper; citation; 커뮤니티; 발견; 논문; 인용정보; 속성정보; attribute

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