Network Structure Insights
mindviral immunity:
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stucture:
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The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mindviral immunity — that is, such network will be more resilient and adaptive than a less diverse one.
In case of a discourse network, high mindviral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the
modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of
influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the
percentage of nodes in the top community.
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Narrative Influence Propagation:
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The chart above shows how influence propagates through the network. Xaxis: lemma to lemma step (narrative chronology). Yaxis: change of influence.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
propagation dynamics:

alpha exponent: (based on Detrended Fluctuation Analysis of influence)
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We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a powerlaw relation in how the influence propagates in this narrative over time (e.g. most of the time noninfluential words, occasionally words with a high influence).
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating  alpha <= 0.65), variable (stationary, has longterm correlations  0.65 < alpha <= 0.85), fractal (adaptive  0.85 < alpha < 1.15), and complex (nonstationary  alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (nearcritical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Degree Distribution:
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Using this information, you can identify whether the network has
scalefree / smallworld (longtail power law distribution) or
random (normal, bellshaped distribution) network properties.
This may be important for understanding the level of
resilience and the dynamics of
propagation in this network. E.g. scalefree networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a powerlaw is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scalefree, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.
KolmogorovSmirnov test compares the distribution above to the "ideal" powerlaw ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.