×  ⁝⁝ 
Export the Data


Network Graph Images:

The graph images for publishing on the web or in a journal. For embeds and URLs use the share menu.
PNG (Image)  SVG (Hi-Res)

Network Graph Data:

The raw data with all the statistics for further analysis in another software.
JSON  CSV  Gexf (Gephi)

Visible Statements (Tagged):

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

All the Text:

Plain text used to create this graph without any meta-data.
Download Plain Text (All Statements)
×
Share Graph Image

 
Share a non-interactive image of the graph only, no text:
Download Image Tweet
 
Share Interactive Text Graph

 

 
×
Save This Graph View:

 

×
Delete This Graph:

 

×
About this Context Graph:

 
total nodes:  extend
 
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.

We all want to go to space. At least I do want to go to space and tomorrow is the 50th anniversary of the moon landing as I am recording this video, so if disappointment and it is absolutely relevant to give a little bit of context around space, but also to tell you how you can go to space today or almost there is a little trick. Of course, the history of innovation is not linear. There are many, many phases and there can be an enormous amount of time between an idea and its application. One of my favorite examples is Leonardo inventing the helicopter. He could have waited and he may be waited all his life for working together with the other experts in order to develop what it was needed to turn his idea into a usable object, but he died without it. Seeing the light.

   edit   deselect   + to AI

 

It took another 500 years before we could have viable helicopters that safely and economically could transport us from one place to another, or imagine a degrade a geographical explorer and their ships that were dangerous and unreliable, but still allowed those who dared to discover a new continence after Columbus Vij that allowed Europeans to find India first they thought. And then we had a Amerigo Vespucci. The continent received its name, America, as we call it today. Well, hundreds of years after, even at the beginning of the 20th century, going from Europe to America was a dangerous one way void for millions of people who dare to take it. So it sounds like we are a little bit spoiled when after the incredible advances of space exploration and human space flight of the 60s we are pretending to be able to go to space with the same convenience, a with the same safety and the same economy of scale with which we are taking a plane to go from city to city, within a continent or across

   edit   deselect   + to AI

 

the entire planet.

   edit   deselect   + to AI

 

It is of course a wonderful desire, but technologies need to consolidate and entire global supply chains need to generate the kind of systems that can support not a few dozen or a few hundred, but millions of people being able to go to space. One of the reasons why this innovation did not happen over the course of the past decades at the pace that it potentially could have is because nation states had a monopoly on space flight, especially on human space flight, whether it was the Soviet Union or the United States, more recently, obviously Russia, but also China in being able to assemble the technologies that allow, delivering the capsule that can support human life beyond the a hundred kilometers limit that conventionally defines being in space and then reaching or beat lower earth orbit or higher orbits, things that we actually didn't do after the Apolo flights ended, uh, at the beginning of the seventies. Well, all of this could only be done by nation states, but today we have a

   edit   deselect   + to AI

 

whole group of private companies that are fighting for customer money and competing with each other in order to deliver a large number of satellites in order to deliver uh, cargo to the International Space Station and also in order to build safe, economical, reliable, flexible options to bring humans in space and in orbit space x, blue origin, virgin galactic are a few of these and hopefully there will be others as well that enrich the number of ideas that can be tested in order to find the ones that work.

   edit   deselect   + to AI

 

And then we'd industrialization with the number of flights increasing, the cost will come down. So how can you go to space today? If you cannot go to space today, you have to find an analog and a fantastic analog that I found a few years ago. And since then I am absolutely fascinated by it and passionate of it, even though I cannot practice it as frequently as I would like to is scuba diving. Think about it, you are in an alien environment. The vacuum of space in one case underwater in the outer, this environment can kill you very, very rapidly in a matter of minutes. The only reason you don't die is because there are all kinds of sophisticated technology solutions that enable you to survive in this alien environment. And even though scuba diving seems something that is fairly mundane, the technologies that are needed in order to allow people to go underwater safely, economically, reliably are pretty recent. We have come up with these technologies over the course of the past few

   edit   deselect   + to AI

 

decades and they are still evolving. So as you scuba dive, you put your life at risk, but in a calculated fashion and you rely on sophisticated technologies in order to be able and survive the experience. But there is more. One of the defining features of being in space is absence of gravity. Actually the force of gravity, Eh, definitely. Yeah.

   edit   deselect   + to AI

 

Come here, Mel [inaudible]. Actually, the force of gravity still is acting on your body and the body of the space craft the carrying you. But exactly because you are both in freefall. Uh, you don't perceive that and your relative motions are the same. So whether you are floating or a droplet of water, um, very spectacularly recorded in the, in the many videos that we see from the International Space Station or actually any other experiment in special relativity tells us that there is no way of distinguishing between, uh, being an absence of gravity or being in free full, uh, many objects. Your reference frames specifically together with you and underwater is a little bit lag that you are not in free fall, but you are floating and you're floating is, um, managed by your gear, um, that you inflate or deflate, uh, in order to keep your buoyancy neutral at various depths.

   edit   deselect   + to AI

 

Uh, you adjusted. And the adaptation is incredibly fast as demonstrated by the fact that after typically 40, 50 minutes of a simple, uh, beginner's grade scuba diving, when you climb the back in the boat with your gas tank on your back and your wet suit, the force of gravity that you didn't feel for an hour hits you that maybe is not as strong as the sensation of the astronauts and cosmonauts when they come back from extended space flights. But it is still, um, the rapid adaptation of your body to the absence of gravity on underwater compared to when you go back on the boat and everything feels so heavy. And then after an hour or two, the sensation disappears. So I highly recommend it and in order to illustrate it, here is a fun video. Um, that was shot and edited and published originally, I think three years ago by my son. Cosimo that vividly illustrates how exhilarating the experience of scuba diving is. Do you want to go to space? Go Scuba diving. Don't stop, don't stop.

   edit   deselect   + to AI

 

I hope you liked this video, and if you do, you can support me on Patreon in order to record more of these videos about the context of the themes that we are seeing in the world around us. With as little as $5 a month, you can become a supporter with exclusive content, dedicated Q and A's and live opportunities online as well as offline to meet with me and you will help my crew edit, record, upload these videos with a frequency of one a week. Thank you for watching this episode of the context and see you next week. A one shot with just one mistake.

   edit   deselect   + to AI

 


        
Show Nodes with Degree > 0:

0 0

Filter Graphs:


Filter Time Range
from: 0
to: 0


Recalculate Metrics Reset Filters
Show Labels for Nodes > 0 size:

0 0

Default Label Size: 0

0 20



Edges Type:



Layout Type:


 

Reset to Default
Language Processing Settings:

language logic: stop words:
 
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse structure:
×  ⁝⁝ 
×  ⁝⁝ 
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.
×  ⁝⁝ 
     
Main Topical Groups:

please, add your data to display the stats...
+     full stats   ?     show categories

The topics are 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   ?

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 a 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 as it allows us to better detect general patterns.

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
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.
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
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

     advanced settings    add data manually

Sign Up