The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Network Extraction of Academic Researchers
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
A probabilistic topic-connection model for automatic image annotation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Citation author topic model in expert search
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Academic network analysis: a joint topic modeling approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.