Information Theory of Complex Networks: on evolution and architectural constraints paper by Sole and Valverde (2004) features a very interesting chart that shows how different types of networks relate to each other in terms of their randomness, heterogeneity, and modularity.
This is a report on the experiment that Nodus Labs conducted on some of the more active Russian protest Facebook groups formed after the rigged Russian election in 2011. We made two network visualizations for three different protest groups over a period of one month in order to observe their dynamics. We found that the most influential members of these groups were not too politically engaged before the elections and were mainly journalists, students, event organizers, and media workers. We also found that the groups formed around ideological causes (such as “Putin must leave”) stagnated in their development in January 2012, while the groups formed around a call for active participatory actions (“Volunteers for the fair elections”) have grown in size and density considerably, building a very well connected and yet open network that was able to bring many new members together around their cause.
We visualized Hamlet’s “to be or not to be” as a text network and then read it again using Alexis Jacomy’s GexfWalker. Whether it is a new reading of Shakespeare’s classic or a bunch of unrelated words is for you to decide, but at least it allows for polysingularity of text to be expressed more fully through following the word relations while staying loyal to the text’s original structure.
In this research we propose a method for visualizing text’s polysingularity: the multiple clusters of meaning circulation contained within a text. These clusters can be described as “strange attractors” (to use the term from dynamical systems theory), which are actualized during the process of reading. We use network analysis in order to plot the text’s structure onto a two-dimensional plane and represent these strange attractors as the communities of co-occurring nodes, positioned within the graph depending on their influence for the production of meaning.
In this work we propose a method and algorithm for identifying the pathways for meaning circulation within a text. This is done by visualizing normalized textual data as a graph and deriving the key metrics for the concepts and for the text as a whole using network analysis. The resulting data and graph representation are then used to detect the key concepts, which function as junctions for meaning circulation within a text, contextual clusters comprised of word communities (themes), as well as the most often used pathways for meaning circulation.