Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOrganization of networks with tagged nodes and biased links: a priori distinct communities. The case of Intelligent Design Proponents and Darwinian Evolution Defenders
99
0
0.0
(
0
)
Authors
G. Rotundo
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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 small size network, i.e. that inherent to the ongoing debate between modern creationists (most are Intelligent Design (ID) proponents (IDP)) and Darwins theory of Evolution Defenders (DED)). This study is carried out by analyzing the structural properties of the citation network unfolded in the recent decades by publishing works belonging to members of the two communities. With the aim of capturing the dynamical aspects of the interaction between the IDP and DED groups, we focus on $two$ key quantities, namely, the {it degree of activity} of each group and the corresponding {it degree of impact} on the intellectual community at large. A representative measure of the former is provided by the {it rate of production of publications} (RPP), whilst the latter can be assimilated to the{it rate of increase in citations} (RIC). These quantities are determined, respectively, by the slope of the time series obtained for the number of publications accumulated per year and by the slope of a similar time series obtained for the corresponding citations. The results indicate that in this case, the dynamics can be seen as geared by triggered or damped competition. The network is a specific example of marked heterogeneity in exchange of information activity in and between the communities, particularly demonstrated through the nodes having a high connectivity degree, i.e. opinion leaders.
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 as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation and show that it leads to heavy-tailed inter-event time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems.
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 complex systems described by networks, discovering communities is primarily important for understanding overall functions of these systems. Much effort has been devoted to developing methods to detect communities in networks since the early days of network science. Nevertheless, the method to reveal key characteristics of communities in real-world network remains to be established. Here we formulate decomposition of a random walk spreading over the entire network into local modules as proxy for communities. This formulation will reveal the pervasive structure of communities and their hierarchical organization, which are the hallmarks of real-world networks but are out of reach of most existing methods.
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 directed dependency links impact criticality, we employ this approach to study the percolation properties of networks with both undirected connectivity links and directed dependency links. We find that when a random network with a given degree distribution undergoes a second-order phase transition, the critical point and the unstable regime surrounding the second-order phase transition regime are determined by the proportion of nodes that do not depend on any other nodes. Moreover, we also find that the triple point and the boundary between first- and second-order transitions are determined by the proportion of nodes that depend on no more than one node. This implies that it is maybe general for multiplex network systems, some important properties of phase transitions can be determined only by a few parameters. We illustrate our findings using Erdos-Renyi (ER) networks.
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 into two parts, where the proposed equations are interpreted in terms of removal of a cognitive dissonance by agents placed in the network nodes. There, the signs and values of links refer to positive or negative interpersonal relationships of different strength. Second is an application of a method akin to the previous one, dedicated to communities identification, to the Sierpinski triangle of finite size. During the time evolution, the related graphs are weighted; yet at the end the discrete character of links is restored. In the case of the Sierpinski triangle, the method is supplemented by adding a small noise to the initial connectivity matrix. By breaking the symmetry of the network, this allows to a successful handling of overlapping nodes.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
