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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.

There is a lot of enthusiasm around nsds, non fungible tokens lately, especially among those who have been following Bitcoin and blockchain applications, but they didn't know how to relate to them. Maybe they weren't attracted by the financial aspect or the decentralization, that it could represent and the social, societal implications. But should you care about NF Ts, and why all the interest. Should you now turn everything you have into an NF T What does that even mean the ability to better handle digital objects. Then alternative systems has been a promise of blockchain technologies for long. Bitcoin is digital, money, and the promise of Bitcoin is to be a better money than other alternatives. What are banknotes as a medium of exchange with a gold as a store of value and the promise of nsds is to handle better digital objects, then the alternative in some specific applications, some of which we can already understand, and may or may not be relevant to you and interesting, and

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others that certainly, we still have to discover, let alone, implement and adopt widely. One of the aspects of money that NFT is completely hands handle the opposite way, is what is called fungibility. If I give you a banknote. You are not going to look at the serial number. And let's say that you have a preference for even numbers, and if I give you a banknote with an odd number, you will say, Oh, this is not $10 I will only accept this as $9, even if it says 10, because the serial number is odd, and I don't like odd numbers, or it doesn't happen that you pay something with a banknote, and a gas chromatography exam will highlight that there are cocaine traces on that banknote, and the person will not accept it, because they will say that the banknote has been involved in illegal drug trafficking, as evidenced by that result. This is by the way, true. Most of the dollar and euro banknotes have traces of cocaine on them. That can be revealed. And if we didn't believe that money was

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fungible, and that we shouldn't get into too much detail on what is their a serial number or what traces of chemicals they carry. It would be a big issue because we couldn't handle money. So, money, not only is fungible, but it must be fungible. This is not an absolute, because, for example, if I hand you a banknote. And this banknote while genuine. It is tainted by the particular chemicals in the paints the coloring that the explosion of a charge it procures when there is a bank robbery, and this device is in the bag and as I escape it rolling saw the bank notes. Well, if you are smart you are actually not going

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to accept that bank note, Because it is tainted, it is coming from a robbery, straightaway, it can be directly connected to a robbery. So, the fungibility of money is not absolute, but it is to a very large degree fungible.

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Now,

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what are some of the reasons why certain things may not be fungible.

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Well,

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you could have

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an apartment

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of let's say 100 square meters 300 Sorry 3000 square feet more or less. And unless I'm wrong, whatever it is, from, square meters two square feet. Let's start over. Let's take an apartment of any given size. That particular measure of whatever the area of the apartment is the size of the apartment is, is not enough to be able to compare it with another apartment of the same size. The two are not interchangeable. If you say, Oh, why didn't you stay here, And I give you some money for that apartment. But then something comes up, and you give me another apartment instead I will most likely not be happy at least I will want to learn 100 different things about the new one that you are proposing, as part of the transaction. And this is true for most things that are similar, but not interchangeable. Well most things physical, because on the other hand, If I have my typical picture that I use as my portray online as I sign up on various services. The picture that I upload is going to be jpg

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file and image file. And I upload it once, twice, thrice, four times. And that picture is going to be the same everywhere. The way it is rendered on the particular platform may be different but it is always going to be the same picture. Now, the solution of NF T's, is the ability to transfer the uniqueness of physical objects that are not exchangeable to digital objects that become not exchangeable as a consequence of wrapping them in a digital signature that is managed on the blockchain and makes the digital object, unique. Now, we know and hopefully you know, of all the various features of blockchains of transparency of traceability of a free unfettered participation, and so on. So, as you combine this wrapper around a digital object that makes it unique, with the rest of the features that blockchains Have you achieve something that can be applied in in various areas, and they can be useful. What is almost universally, the application. Today, as we look at NF T's, is to be able to

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show the provenance of a unique digital object, a digital object that is not unique, by itself, it has been made unique, so it must be important that we don't confuse the

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other characteristics of digital objects. With this unique rapper, and the ability to prove the chain of ownership and the provenance of that object. For example, the fact that digital expressions of human creativity, possess a feature called copyright, and that this copyright belongs to the author initially, but it can be transferred is parallel and independent from it being an EFT. If I take my picture, and they wrap it in this digital signature, turn it into an EFT and then sell you that EFT. That may or may not mean that I am also selling you the copyright. The ability to exclusively benefit from the various potential uses of that image. That is a completely independent decision and transaction. Now, as I make a digital object, unique. It is important to understand that the chain of ownership, and the provenance of that digital object as it is created, wrapped sold once, twice, three times five times 50 times and so on, is only the digital reality. If I have to identify a physical

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object, and then give it a digital representation. Sign it. To turn it into an NFT the interface between the physical world and the digital world, will have to be carefully managed independently of the features of NF Ts. The benefit of NF T's accrues only after the information has been acquired. So, if a painting is a physical painting and I wrap it, it's digital representation in an NFT that is sold once or 50 times. If the painting was a fake. And someone appreciates the association of a given an FTA as a signature of the painting. Well, that doesn't make the painting. Genuine. Another application, outside of the art world of NF T's, could be for example in real estate, where each piece of real estate can be associated with an NF T. But once again, all the parameters that describe a house or an apartment, will have to be truthful. For the value of the rapper and the chain of provenance and ownership to be valuable to garbage in, garbage out. In the traditional blockchain parlance,

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this is called the Oracle problem.

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The

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fact that we have to bring in information from the physical world, to the digital world, and, and that interface is something that we can, as of right now only have control over, in terms of trusting the interface, bringing in the right kind of information.

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So

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I experimented with NF T's by actually selling a tweet of mine. That is from 10 years ago. April 23 2011 I tweeted Bitcoin p2p digital currency. I wish you good luck and good fortune, against the ire of nation states everywhere. And so did I sell the copyrights to that sentence. No, what I sold is a chain of connection to the person who now owns the NFT who knows that this transaction is genuine. I wrapped the tweet as if I autographed it, and that person will be able, in a year or 10 years whenever to sell that again, knowing that it indeed has belonged to me, and I gave it or sold it to him, and so on. And the experiment is very easy if you have a Twitter account. You can do it, to just go to the website I used, which is sent.co, if I'm not mistaken, there is a section called valuables you connect your Twitter account in order to prove that indeed the tweets that you are wrapping and selling as an NFT are yours. And then, it is just a question of finding someone who wants to buy

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them. Now, if I were you, I would definitely find someone to buy them, meaning that you just ask a friend, do you mind buying this for $10 or $5, because you want to get your hands dirty and complete the experiment. So I encourage you to go and try it. And then to keep thinking, what are the possible applications of NF T's that go beyond the two that I gave, for me, NF T's are here to stay. They have features that are worthy of exploring further enriching, enhancing just one example. It used to be the case that creators would not benefit from secondary markets. If a painter sold a painting, and that painting became very valuable 10 years. From the first sale. The 100% of that additional value would go to the people who held on to the painting and sold it to the last latest buyer. But when NFT is, you can set up, actually, what is the percentage of royalty, that you will receive from the secondary, tertiary, whatever number of sales, down the line. And death is interesting, for

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example, and I'm sure there will be many other ways that NF T's will be able to prove themselves to be superior to how we have been tracing the nature the ownership and the provenance the chain of custody of objects in the past, more and more of our economies becoming digital and NF T's are an extremely interesting new component that deserve the attention and the enthusiasts awesome that they have now. And, importantly, deserve the experiments that you can conduct easily with them.

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semantic variability:
×  ⁝⁝ 
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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  
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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):
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 ?  

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):
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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:
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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
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stucture:
N/A
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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 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:
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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:

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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):

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

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