Core-periphery structure in sectoral international trade networks: A new approach to an old theory.

The research on core-periphery structure of global trade from a complex-network perspective has shown that the world system is hierarchically organized into blocks and that countries play different roles in the world economy. Yet, little attention has been paid to investigating whether the sectoral international trade networks conform to a core-periphery structure, hence what is the role of different levels of processing in creating and maintaining structural inequality. This issue is of particular importance given the contemporary focus upon global production networks and reshaping of the international division of labor. With this in mind, we propose a model (LARDEG) from network science to reexamine old theories in economics, such as core-periphery structures in sectoral international trade networks and test whether the global value chains have changed structural positions in terms of the level of processing. The economic background of our model permitting a more accurate sorting of countries into structural positions and the general stability of results have provided for a more solid measurements than has hereto been possible. Our algorithm naturally produces networks with hierarchically nested block structure obtained from an iterative decomposition of the network periphery such that each block represents a vertex set of a maximal size sub-graph existing at different levels. The results not only lend support to the previous hierarchical model of the world-system (core, semi-periphery, and periphery) but also find that, depending on particular industry, the number of analytically identifiable blocks could be more than three. We show that 'size effect' is the one that prevails for core block membership at the first hierarchical level, while the GNI per capita is a much poorer proxy for the world-system status. Moreover, the patterns of blocks we label as the second- or third-level 'core' are strongly dependent on distance and geographical proximity. Overall, the various configurations of asymmetrical trade patterns between our blocks and the remarkably stable position of core countries at the top of structure clearly indicate that the rise of global production networks has actually restored a huge and unequal international division of labor splitting the world into 'headquarter' and 'factory' economies.

Bayesian consensus clustering in multiplex networks.

Multiplex networks are immanently characterized with heterogeneous relations among vertices. In this paper, we develop Bayesian consensus stochastic block modeling for multiplex networks. The posterior distribution of the model is approximated via Markov chain Monte Carlo, and a Gibbs sampler is derived in detail. The model allows both integrated analysis of heterogeneous relations, thus providing more accurate block assignments, and simultaneously handling uncertainty in the model parameters. Motivated by the fact that the symmetry in physics plays a crucial role, we discuss also the symmetry in statistics, which is nowadays commonly known as exchangeability-the concept that has recently transformed the field of statistical network analysis.