The role identification in social networks is important for analyzing and understanding social networks, predicting user behavior, and researching relationships and interactions between users. Most role identification works are based on link analysis following the ideas of PageRank and HITS, and some methods also combing link analysis and content analysis. However, previous role identification methods focus on the amount of topic released by the target user without considering the importance of the topic. In order to identify high-value users by topic weights in social networks, this paper first proposes a method of assigning topic weights based on the LDA model and the collective credit allocation method in science(CCA). Then we introduce content value density based on the Kullback-Leibler divergence of topic weight and topic distribution to rank the high-value users. In addition, the new method is more suitable for calculation on large-scale networks and time-evolution networks because the new method is an inductive method which works on the local graph. Finally, the experiments are carried out to analyze the effect of the topic weight allocation and content value density in a real data set. The results show that the new method is superior to the compared methods when identifying high-value users in certain circumstances. INDEX TERMS Social network, role identification, high-value user, topic weight.

In this study, we developed an i-ELISA based on epitope AA503-509 (RANEPKE), which is on nonstructural protein of MDPV. This i-ELISA could be used as a diagnostic tool for differentiating infected Muscovy ducks from Muscovy ducks vaccinated with inactivated virus.

No abstract

Development of a sandwich enzyme-linked immunosorbent assay based on single-domain antibody for detecting goose parvovirus infection[Desenvolvimento de um ensaio imunoadsorvente ligado a enzima sandwich baseado em anticorpo de domínio único para detecção de infecção por parvovírus de ganso]