A New Method of Identifying Core Designers and Teams Based on the Importance and Similarity of Networks.

In the process of product collaborative design, the association between designers can be described by a complex network. Exploring the importance of the nodes and the rules of information dissemination in such networks is of great significance for distinguishing its core designers and potential designer teams, as well as for accurate recommendations of collaborative design tasks. Based on the neighborhood similarity model, combined with the idea of network information propagation, and with the help of the ReLU function, this paper proposes a new method for judging the importance of nodes-LLSR. This method not only reflects the local connection characteristics of nodes but also considers the trust degree of network propagation, and the neighbor nodes' information is used to modify the node value. Next, in order to explore potential teams, an LA-LPA algorithm based on node importance and node similarity was proposed. Before the iterative update, all nodes were randomly sorted to get an update sequence which was replaced by the node importance sequence. When there are multiple largest neighbor labels in the propagation process, the label with the highest similarity is selected for update. The experimental results in the related networks show that the LLSR algorithm can better identify the core nodes in the network, and the LA-LPA algorithm has greatly improved the stability of the original LPA algorithm and has stably mined potential teams in the network.

EBglmnet: a comprehensive R package for sparse generalized linear regression models.

SUMMARY: EBglmnet is an R package implementing empirical Bayesian method with both lasso (EBlasso) and elastic net (EBEN) priors for generalized linear models. In our previous studies, both EBlasso and EBEN outperformed other state-of-the-art methods such as lasso and elastic net in inferring sparse genotype and phenotype associations, in which the number of covariates is typically much larger than the sample size. While high density genetic markers can be easily obtained nowadays in genetics and population analysis thanks to the advancements in molecular high throughput technologies, EBglmnet will be a very useful tool for statistical modeling in this area. AVAILABILITY AND IMPLEMENTATION: EBglmnet package is freely available from the R archive CRAN (http://cran.r-project.org/).