Identifying xe2x80x98associatedsleepingbeautiesxe2x80x99 in xe2x80x98swangroupsxe2x80x99 based on small qualified datasets of physics and economics xe2x80x98Sleeping beautiesxe2x80x99 refer to the papers that received no or scarce citation after appearance, but considerable citations several years later, which is a special citation pattern. Following two new citation models called xe2x80x98blackxe2x80x93white swansxe2x80x99 and xe2x80x98swangroupsxe2x80x99 that described interactional citation phenomena in the key publications of Nobel laureates, we find that there are xe2x80x98associatedsleepingbeautiesxe2x80x99 in xe2x80x98swangroupsxe2x80x99 in the field of physics and economics, which means that the phenomena are established in both science and social science. In practical datasets 1995xe2x80x932010, there are 12% and 11% xe2x80x98associatedsleepingbeautiesxe2x80x99 in xe2x80x98swangroupsxe2x80x99 of physics and economics, respectively. Moreover, we extend data in discipline of medicine to do further validation, and 2 xe2x80x98associatedsleepingbeautiesxe2x80x99 are discovered.

OverlapLDA: A Generative Approach for LiteratureBased Discovery Literaturebased discovery, the process of choosing bridge terms to build plausible connections between xe2x80x9cunrelatedxe2x80x9d terms, helps the biomedical research by generating potentially useful hypotheses. The basic idea of previous work is conducting two or higher order association rules searching and ranking, either according to the semantics or the statistical measurements. However, the high order searching nature makes current approaches computationally complex. We address this problem by proposing a generative model assuming all the connection words as a rank of words. We further present an inference method for the generative model to learn the rank. Our model avoids the process of high order searching. Experiments show that our approach addresses the problem both effectively and efficiently.