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Among topics of opinion formation it is of interest to observe the characteristics of networks with a priori distinct communities. As an illustration, we report on the citation network(s) unfolded in the recent decades through web available works belonging to selected members of the Neocreationist and Intelligent Design Proponents (IDP) and the Darwinian Evolution Defenders (DED) communities. An adjacency matrix of tagged nodes is first constructed; it is not symmetric. A generalization of considerations pertaining to the case of networks with biased links, directed or undirected, is thus presented. The main characteristic coefficients describing the structure of such partially directed networks with tagged nodes are outlined. The structural features are discussed searching for statistical aspects, equivalence or not of subnetworks through the degree distributions, each network assortativity, the global and local clustering coefficients and the Average Overlap Indices. The various closed and open triangles made from nodes, moreover distinguishing the community, are especially listed to calculate the clustering characteristics. The distribution of elements in the rectangular submatrices are specially examined since they represent inter-community connexions. The emphasis being on distinguishing the number of vertices belonging to a given community. Using such informations one can distinguish between opinion leaders, followers and main rivals and briefly interpret their relationships through psychological-like conditions intrinsic to behavior rules in either community. Considerations on other controversy cases with similar social constraints are outlined, as well as suggestions on further, more general, work deduced from our observations on such networks.
Social impacts and degrees of organization inherent to opinion formation for interacting agents on networks present interesting questions of general interest from physics to sociology. We present a quantitative analysis of a case implying an evolving
The concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes a
In network science, a group of nodes connected with each other at higher probability than with those outside the group is referred to as a community. From the perspective that individual communities are associated with functional modules constituting
The self-consistent probabilistic approach has proven itself powerful in studying the percolation behavior of interdependent or multiplex networks without tracking the percolation process through each cascading step. In order to understand how direct
Algorithms for search of communities in networks usually consist discrete variations of links. Here we discuss a flow method, driven by a set of differential equations. Two examples are demonstrated in detail. First is a partition of a signed graph i