Co-authorship network structure analysis

The analysis of networks of collaboration between scientists reveals features of academic communities that help in understanding the specifics of collaborative scientific work and identifying the notable researchers. In these networks, the set of nodes consists of authors and there exists a link between two authors if they have coauthored one or more papers. This article presents an analysis of the co-authorship network based on bibliometric data retrieved from the distributed economic database. Here we use the simple network model without taking into account the strength of collaborative ties. The data were analyzed using statistical techniques in order to get such parameters as the number of papers per author, the number of authors per paper, the average number of coauthors per author and collaboration indices. We show that the largest component occupies near 90 % of the network and the node degree distribution follows a power-law. The study of typical distances between nodes and the degree of clustering makes it possible to classify the network as a ‘small world’ network.

Forward genetics combined with unsupervised classifications identified zebrafish mutants affecting biliary system formation.

Impaired formation of the biliary network can lead to congenital cholestatic liver diseases; however, the genes responsible for proper biliary system formation and maintenance have not been fully identified. Combining computational network structure analysis algorithms with a zebrafish forward genetic screen, we identified 24 new zebrafish mutants that display impaired intrahepatic biliary network formation. Complementation tests suggested that these 24 mutants affect 24 different genes. We applied unsupervised clustering algorithms to classify the recovered mutants into three classes unbiasedly. Further computational analyses revealed that each of the recovered mutations in these three classes shows a unique effect on node subtype composition and connection property distribution of the intrahepatic biliary network. Besides, we found that most recovered mutations are viable. In those mutant fish, biliary network phenotypes persist into adulthood, which themselves are good animal models to study chronic cholestatic liver diseases. Altogether, this study provides unique genetic and computational toolsets that advance our understanding of the molecular pathways leading to biliary system malformation and cholestatic liver diseases.

An Algorithm Based on Loop Cutting Contribution Function for Loop Cutset Problem in Bayesian Network

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node pairs, and the parameter ω to control the selection conditions of the node pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm; the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.