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Project Notes:
InfraNodus
Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

people who search for #Google also search for #Apple_Inc

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people who search for #Google also search for #AOL

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people who search for #Google also search for #Tumblr

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people who search for #Google also search for #PayPal

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people who search for #Google also search for #MailRu

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people who search for #Google also search for #Microsoft_Corporation

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people who search for #Google also search for #Digg

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people who search for #Tumblr also search for #SoundCloud

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people who search for #Tumblr also search for #StumbleUpon

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people who search for #Tumblr also search for #Myspace

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people who search for #Tumblr also search for #Digg

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people who search for #Tumblr also search for #Google

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people who search for #Tumblr also search for #MailRu

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people who search for #Tumblr also search for #XING

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people who search for #Digg also search for #StumbleUpon

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people who search for #Digg also search for #Technorati

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people who search for #Digg also search for #Myspace

   edit   deselect   + to AI

 

people who search for #Digg also search for #MailRu

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people who search for #Digg also search for #Tumblr

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people who search for #Digg also search for #Newsvine

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people who search for #Digg also search for #Google

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people who search for #aol also search for #Comcast

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people who search for #aol also search for #Yahoo!

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people who search for #aol also search for #Verizon_Communications

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people who search for #aol also search for #Apple_Inc

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people who search for #aol also search for #Univision

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people who search for #aol also search for #Myspace

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people who search for #aol also search for #ATT

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people who search for #Apple_Inc also search for #Verizon_Wireless

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people who search for #Apple_Inc also search for #O2

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people who search for #Apple_Inc also search for #Sprint_Corporation

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people who search for #Apple_Inc also search for #Amazoncom

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people who search for #Apple_Inc also search for #Sony_Corporation

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people who search for #Microsoft_Corporation also search for #Nokia

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people who search for #Microsoft_Corporation also search for #Logitech

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people who search for #Microsoft_Corporation also search for #Cisco_Systems_Inc

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people who search for #Microsoft_Corporation also search for #Nvidia

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people who search for #Microsoft_Corporation also search for #Bungie

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people who search for #Microsoft_Corporation also search for #Apple_Inc

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people who search for #Microsoft_Corporation also search for #IBM

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people who search for #PayPal also search for #United_States_Postal_Service

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people who search for #PayPal also search for #Amazoncom

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people who search for #PayPal also search for #Payoneer

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people who search for #PayPal also search for #Yahoo!

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people who search for #PayPal also search for #American_Express

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people who search for #PayPal also search for #United_Parcel_Service

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people who search for #PayPal also search for #DHL_Express

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people who search for #MailRu also search for #XING

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people who search for #MailRu also search for #Yandex

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people who search for #MailRu also search for #AddToAny

   edit   deselect   + to AI

 

people who search for #MailRu also search for #Hatena

   edit   deselect   + to AI

 

people who search for #MailRu also search for #Newsvine

   edit   deselect   + to AI

 

people who search for #MailRu also search for #StumbleUpon

   edit   deselect   + to AI

 

people who search for #MailRu also search for #Digg

   edit   deselect   + to AI

 

people who search for #Amazoncom also search for #Flipkart

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people who search for #Amazoncom also search for #Barnes_&_Noble

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people who search for #Amazoncom also search for #Netflix

   edit   deselect   + to AI

 

people who search for #Amazoncom also search for #Apple_Inc

   edit   deselect   + to AI

 

people who search for #Amazoncom also search for #Nike_Inc

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people who search for #Amazoncom also search for #The_Book_Depository

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people who search for #Amazoncom also search for #Adidas

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people who search for #Yahoo also search for #PayPal

   edit   deselect   + to AI

 

people who search for #Yahoo also search for #FC2

   edit   deselect   + to AI

 

people who search for #Yahoo also search for #Rakuten

   edit   deselect   + to AI

 

people who search for #Yahoo also search for #Myspace

   edit   deselect   + to AI

 

people who search for #Yahoo also search for #Apple_Inc

   edit   deselect   + to AI

 

people who search for #Yahoo also search for #AOL

   edit   deselect   + to AI

 

people who search for #Yahoo also search for #Baidu

   edit   deselect   + to AI

 

people who search for #Myspace also search for #Hi5_Networks_Inc

   edit   deselect   + to AI

 

people who search for #Myspace also search for #StumbleUpon

   edit   deselect   + to AI

 

people who search for #Myspace also search for #Digg

   edit   deselect   + to AI

 

people who search for #Myspace also search for #Tumblr

   edit   deselect   + to AI

 

people who search for #Myspace also search for #SoundCloud

   edit   deselect   + to AI

 

people who search for #Myspace also search for #ReverbNation

   edit   deselect   + to AI

 

people who search for #Myspace also search for #AOL

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #Digg

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #Myspace

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #Tumblr

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #XING

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #MailRu

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #Newsvine

   edit   deselect   + to AI

 

people who search for #StumbleUpon also search for #Hatena

   edit   deselect   + to AI

 

people who search for #Xing also search for #MailRu

   edit   deselect   + to AI

 

people who search for #Xing also search for #Hatena

   edit   deselect   + to AI

 

people who search for #Xing also search for #AddToAny

   edit   deselect   + to AI

 

people who search for #Xing also search for #Newsvine

   edit   deselect   + to AI

 

people who search for #Xing also search for #StumbleUpon

   edit   deselect   + to AI

 

people who search for #Xing also search for #Baidu

   edit   deselect   + to AI

 

people who search for #Xing also search for #Digg

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #AddToAny

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #XING

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #MailRu

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #StumbleUpon

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #Digg

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #Hatena

   edit   deselect   + to AI

 

people who search for #Newsvine also search for #Technorati

   edit   deselect   + to AI

 

people who search for #Hatena also search for #XING

   edit   deselect   + to AI

 

people who search for #Hatena also search for #MailRu

   edit   deselect   + to AI

 

people who search for #Hatena also search for #StumbleUpon

   edit   deselect   + to AI

 

people who search for #Hatena also search for #Baidu

   edit   deselect   + to AI

 

people who search for #Hatena also search for #Newsvine

   edit   deselect   + to AI

 

people who search for #Hatena also search for #Digg

   edit   deselect   + to AI

 

people who search for #Hatena also search for #Sina_Corp

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #PayPal

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #Skrill

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #Neteller

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #oDesk

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #MasterCard

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #Elance

   edit   deselect   + to AI

 

people who search for #Payoneer also search for #ClickBank

   edit   deselect   + to AI

 

people who search for #Apple_Inc also search for #ATT

   edit   deselect   + to AI

 

people who search for #ATT also search for #Sprint_Corporation

   edit   deselect   + to AI

 

people who search for #ATT also search for #Verizon_Communications

   edit   deselect   + to AI

 

people who search for #ATT also search for #Apple_Inc

   edit   deselect   + to AI

 

people who search for #ATT also search for #Comcast

   edit   deselect   + to AI

 

people who search for #ATT also search for #DIRECTV

   edit   deselect   + to AI

 

people who search for #ATT also search for #Time_Warner_Cable

   edit   deselect   + to AI

 

people who search for #ATT also search for #Dish_Network

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #Snapdeal

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #HomeShop18

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #Amazoncom

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #eBay

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #Micromax_Mobile

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #Samsung_Telecommunications

   edit   deselect   + to AI

 

people who search for #Flipkart also search for #Rediffcom

   edit   deselect   + to AI

 

people who search for #Netflix also search for #Hulu

   edit   deselect   + to AI

 

people who search for #Netflix also search for #Amazoncom

   edit   deselect   + to AI

 

people who search for #Netflix also search for #Redbox

   edit   deselect   + to AI

 

people who search for #Netflix also search for #HBO

   edit   deselect   + to AI

 

people who search for #Netflix also search for #Comcast

   edit   deselect   + to AI

 

people who search for #Netflix also search for #Vudu

   edit   deselect   + to AI

 

people who search for #Netflix also search for #American_Broadcasting_Company

   edit   deselect   + to AI

 

people who search for #Comcast also search for #DIRECTV

   edit   deselect   + to AI

 

people who search for #Comcast also search for #Verizon_Wireless

   edit   deselect   + to AI

 

people who search for #Comcast also search for #AT&T

   edit   deselect   + to AI

 

people who search for #Comcast also search for #Dish_Network

   edit   deselect   + to AI

 

people who search for #Comcast also search for #Verizon_FiOS

   edit   deselect   + to AI

 

people who search for #Comcast also search for #CenturyLink

   edit   deselect   + to AI

 

people who search for #Comcast also search for #Netflix

   edit   deselect   + to AI

 

#Comcast - Comcast Corporation is an American mass media company and is the largest broadcasting and cable company in the world by revenue.

   edit   deselect   + to AI

 

#Hatena is an internet services company in Japan. It operates various services including the most popular social bookmarking service in Japan, Hatena Bookmark. Hatena is the collective name of the company's services.

   edit   deselect   + to AI

 

#XING is a social software platform for enabling a small-world network for professionals. The company claims that it is used by people from over 200 countries.

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#Newsvine is a community-powered, collaborative journalism news website which draws content from its users and syndicated content from mainstream sources such as The Associated Press

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semantic variability:
×  ⁝⁝ 
×  ⁝⁝ 
Semantic Variability Score
— modulates diversity of the discourse network  how it works?
The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:

N/A

We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).

  1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
  2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
  3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
  4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).

Read more in the cognitive variability help article.
Generate AI Suggestions
Your Workflow Variability:
 
Shows to what extent you explored all the different states of the graph, from uniform and regular to fractal and complex. Read more in the cognitive variability help article.

You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
Phases to Explore:
AI Suggestions  
×  ⁝⁝ 
     
Main Topical Clusters:

please, add your data to display the stats...
+     full table   ?     Show Categories

The topical clusters are comprised of the nodes (words) that tend to co-occur together in the same context (next to each other).

We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
Most Influential Elements:
please, add your data to display the stats...
+     Reveal Non-obvious   ?

AI Paraphrase Graph

We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Network Structure:
N/A
?
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.


Reset Graph   Export: Show Options
Action Advice:
N/A
Structural Gap
(ask a research question that would link these two topics):
N/A
Reveal the Gap   ?   Generate an AI Question
 
A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

Latent Topical Brokers
(less visible terms that link important topics):
N/A
?

These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

Emerging Keywords
N/A

Evolution of Topics
(number of occurrences per text segment) ?
The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.

Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

 
Main Topics
(according to Latent Dirichlet Allocation):
loading...
 ?  

LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):
loading...

We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

Sentiment Analysis


positive: | negative: | neutral:
reset filter    ?  

We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

The approach is based on AFINN and Emoji Sentiment Ranking

 
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.

Keyword Relations Analysis:

please, select the node(s) on the graph see their connections...
+   ⤓ download CSV   ?

Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
Top Relations / Bigrams
(both directions):

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.

As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).

Text Statistics:
Word Count Unique Lemmas Characters Lemmas Density
0
0
0
0
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Network Structure Insights
 
mind-viral immunity:
N/A
  ?
stucture:
N/A
  ?
The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

In case of a discourse network, high mind-viral 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.

Modularity
0
Influence Distribution
0
%
Topics Nodes in Top Topic Components Nodes in Top Comp
0
0
%
0
0
%
Nodes Av Degree Density Weighed Betweenness
0
0
0
0
 

Narrative Influence Propagation:
  ?
The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: 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) ?   show the chart
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 power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential 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 long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical 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:
  calculate & show   ?
(based on kolmogorov-smirnov test) ?   switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law 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.
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