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Author-conference topic-connection model for academic network search
Conference proceeding

Author-conference topic-connection model for academic network search

Jianwen Wang, Xiaohua Hu, Xinhui Tu and Tingting He
Proceedings of the 21st ACM international conference on information and knowledge management, pp 2179-2183
29 Oct 2012
DOI: https://doi.org/10.1145/2396761.2398597

Abstract

academic network search gibbs sampling topic model
This paper proposes a novel topic model, Author-Conference Topic-Connection (ACTC) Model for academic network search. The ACTC Model extends the author-conference-topic (ACT) model by adding subject of the conference and the latent mapping information between subjects and topics. It simultaneously models topical aspects of papers, authors and conferences with two latent topic layers: a subject layer corresponding to conference topic, and a topic layer corresponding to the word topic. Each author would be associated with a multinomial distribution over subjects of conference (eg., KM, DB, IR for CIKM 2012), the conference(CIKM 2012), and the topics are respectively generated from a sampled subject. Then the words are generated from the sampled topics. We conduct experiments on a data set with 8,523 authors, 22,487 papers and 1,243 conferences from the well-known Arnetminer website, and train the model with different number of subjects and topics. For a qualitative evaluation, we compare ACTC with three others models LDA, Author-Topic (AT) and ACT in academic search services. Experiments show that ACTC can effectively capture the semantic connection between different types of information in academic network and perform well in expert searching and conference searching.

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9 citations in Scopus

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