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Every word will be converted to its lemma (e.g. bricks > brick, taken > take) and will be shown on the graph as a node. Set to your language for more precise results. Switch off to turn off lemmatization and add your custom stop words list below.
 
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If you'd like some words to appear as one node on the graph, in addition to your default global synonyms list, list the synonyms, one per line.
Example:
machine:machine learning
learning:machine learning

 

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Dynamic Graph Settings


See the dynamic evolution of this graph: scroll or "play" the text entries to see how the text propagated through the network graph over time.

the final graph

highlight propagation edge
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Visible Statements (Tagged):

Export the currently filtered (visible) statements with all the meta-data tags (topics, sentiment).
CSV (Spreadsheet)   MD (e.g.Obsidian)  

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The raw data with all the statistics for further analysis in another software.
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All the Text:

Plain text used to create this graph without any meta-data.
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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.

takeoff is optional but landing is mandatory.

   edit   deselect   + to AI

 

we have to use the reins of reason on the horse of emotion.

   edit   deselect   + to AI

 

Moderate stress enhances learning. When two neurons fire together, they become wired together. When a strong and weak neuron—call them Al and Betty—stimulate a third neuron—call it Charlie—at the same time, the weak one, Betty, gains the ability to stimulate Charlie to fire. That’s why the ringing of a bell could cause Pavlov’s dog to salivate even when there was no food present. Scientists, with their ever playful juggling of three or four languages at once, call that long-term potentiation (LTP). So risk is an integral part of life and learning. A baby who doesn’t walk, for example, will never risk falling. But in exchange for taking

   edit   deselect   + to AI

 

We think we believe what we know, but we only truly believe what we feel.

   edit   deselect   + to AI

 

But in the long course of evolution, it has been a successful strategy.

   edit   deselect   + to AI

 

Perceptions come at you like the 6 million hits you get when you do an Internet search. Without a powerful search engine, you’re paralyzed. One search engine involves emotional bookmarks, in which feelings help direct logic and reason to a place where they can do useful work. A second strategy the brain uses for handling complicated problems is to create mental models, stripped-down schematics of the world.

   edit   deselect   + to AI

 

In more subtle tricks, the magician creates a mental model for you, a short-term memory of the world.

   edit   deselect   + to AI

 

One of the reasons magic tricks work can be explained through a brain system called working memory. It is a general purpose workspace, and most of us experience it as attention or conscious thought. In addition, there are specialized systems for verbal and nonverbal information, and they have a type of short-term memory that allows perceptions to be compared with one another over the span of a few seconds. The general purpose area can take in information from the specialized systems (sight, smell, sound, and so on) and can integrate and process that information through what LeDoux calls “an executive function.” That

   edit   deselect   + to AI

 

Working memory can also retrieve information from long-term memory. The fact that you can read this long sentence is the result of your working memory’s ability to hold the beginning, middle, and end all at once and to retrieve definitions and associations from long-term memory and use them to make sense of the words. It is also the result of the fact that you have created mental models of the words.

   edit   deselect   + to AI

 

But the model he created before the plane appeared did not contain the plane.

   edit   deselect   + to AI

 

If things don’t go according to the plan, revising such a robust model may be difficult. In an environment that has high objective hazards, the longer it takes to dislodge the imagined world in favor of the real one, the greater the risk. In nature, adaptation is important; the plan is not. It’s a Zen thing. We must plan. But we must be able to let go of the plan, too.

   edit   deselect   + to AI

 

The rigid person is a disciple of death; The soft, supple, and delicate are lovers of life.

   edit   deselect   + to AI

 

In system accidents, unexpected interactions of forces and components arise naturally out of the complexity of the system. Such accidents are made up of conditions, judgments, and acts or events that would be inconsequential by themselves. Unless they are coupled in just the right way and with just the right timing, they pass unnoticed.

   edit   deselect   + to AI

 

Perrow used technical terms to describe those systems. He called them “tightly coupled.” He said that they must be capable of producing unintended complex interactions among components and forces. In his view, unless the system is both tightly coupled and able to produce such interactions, no system accident can happen (though other failures happen all the time).

   edit   deselect   + to AI

 

He’s talking about a theory called “risk homeostasis.” The theory says that people accept a given level of risk. While it’s different for each person, you tend to keep the risk you’re willing to take at about the same level. If you

   edit   deselect   + to AI

 

The person who has the best chance of handling a situation well is usually the one with the best…mental pictures or images of what is occurring outside of the body.”

   edit   deselect   + to AI

 

Survivors aren’t fearless. They use fear: they turn it into anger and focus.

   edit   deselect   + to AI

 

The trick is to become extremely stingy with your scarce resources, balancing risk and reward, investing only in efforts that offer the biggest return.

   edit   deselect   + to AI

 

Helping someone else is the best way to ensure your own survival. It takes you out of yourself. It helps you to rise above your fears. Now you’re a rescuer, not a victim. And seeing how your leadership and skill buoy others up gives you more focus and energy to persevere. The cycle reinforces itself: You buoy them up, and their response buoys you up. Many people who survive alone report that they were doing it for someone else (a wife, boyfriend, mother, son) back home.

   edit   deselect   + to AI

 

Purpose is a big part of survival, but it must be accompanied by work.

   edit   deselect   + to AI

 

The survivor plans by setting small, manageable goals and then systematically achieving them.

   edit   deselect   + to AI

 

The birds are the radar of the forest.”

   edit   deselect   + to AI

 

ways of seeing and walking that were used by Native American trackers and other Aboriginal peoples. He called it “Owl Eyes and the Fox Walk,” that full-body alertness

   edit   deselect   + to AI

 

certain people, when afraid, experience “activation of the amygdala [which] will lead working memory to receive a greater number of inputs, and inputs of a greater variety, than in the presence of emotionally neutral stimuli.”

   edit   deselect   + to AI

 

Like an immune system, it defined the inside and the outside. And by being responsible to each

   edit   deselect   + to AI

 

“A pattern of movements developed after my initial wobbly hops and I meticulously repeated the pattern. Each pattern made up one step across the slope and I began to feel detached from everything around me. I thought of nothing but the patterns.” His struggle had become a dance, and the dance freed him from the terror of what he had to do.

   edit   deselect   + to AI

 

Countless survivors have reported the same thing: by developing a pattern and then fixing on nothing but making the pattern perfect, they were able to get out of seemingly impossible situations.

   edit   deselect   + to AI

 

Survival is adaptation, and adaptation is change,

   edit   deselect   + to AI

 

Perceive, believe (look, see, believe).

   edit   deselect   + to AI

 

Stay calm (use humor, use fear to focus).

   edit   deselect   + to AI

 

survivors use patterns and rhythm to move forward in the survival voyage,

   edit   deselect   + to AI

 

Scientists, with their ever playful juggling of three or four languages at once, call that long-term potentiation (LTP). So risk is an integral part of life and learning

   edit   deselect   + to AI

 

He’s talking about a theory called “risk homeostasis.” The theory says that people accept a given level of risk. While it’s different for each person, you tend to keep the risk you’re willing to take at about the same level. If you

   edit   deselect   + to AI

 

Working memory can also retrieve information from long-term memory. The fact that you can read this long sentence is the result of your working memory’s ability to hold the beginning, middle, and end all at once and to retrieve definitions and associations from long-term memory and use them to make sense of the words. It is also the result of the fact that you have created mental models of the words.

   edit   deselect   + to AI

 

Perrow used technical terms to describe those systems. He called them “tightly coupled.” He said that they must be capable of producing unintended complex interactions among components and forces. In his view, unless the system is both tightly coupled and able to produce such interactions, no system accident can happen (though other failures happen all the time).

   edit   deselect   + to AI

 

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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
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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
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Influence Distribution
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%
Topics Nodes in Top Topic Components Nodes in Top Comp
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Nodes Av Degree Density Weighed Betweenness
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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.
Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

 
We will build two graphs:
1) Google search results for your query;
2) Related searches for your query (Google's SERP);
Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
Please, enter a search query to discover what else people are searching for (from Google search or AdWords suggestions):

 
We will build a graph of the search phrases related to your query (Google's SERP suggestions).
Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

 
We will build two graphs:
1) the content that already exists when you make this search query (informational supply);
2) what else people are searching for when they make this query (informational demand);
You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
Please, enter your query to visualize Google search results as a graph, so you can learn more about this topic:

   advanced settings    add data manually
Discover the main topics, recurrent themes, and missing connections in any text or an article:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in open survey responses:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in customer product reviews:  
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

     advanced settings    add data manually

Enter a topic or a @user to analyze its social network on Twitter:

 advanced settings    add data manually

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