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InfraNodus
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Welcome to this new episode of the context. My Name is David Orban and I will be talking to you about jolting technologies. There is a paradigm shift moving from accelerating technological change to jolting technological change. What is a jolt? Technically, the jolt is the measure of the rate of change of acceleration. Mathematically, it is the third derivative of position. The first derivative is velocity as a vector or speed as an absolute measure. The second derivative is acceleration and the third derivative is jolt. Let me give you immediately a couple of examples to show that this is not just a abstract and use less in terms of wanting to go further and uh, dealing with quantities that have nothing to do with our, uh, world, but living mathematical abstraction. The first example is that of a variable acceleration. Imagine a rocket. The engines are at full thrust as a sense, but contrary to many other forms of transportation, the rocket brings on board both, uh, it's a, uh,

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propellant and it's oxidant. And as they chemically combine the exhaust gas based on the third low of Newton gives the reaction that accelerates the rocket. However, what happens is that this acceleration is proportional to actually the mass of the rocket. It would be also proportional to the force, but we said that the engine is at maximum power. So it will be constant and as the rocket consumes it's propellant and oxidant combining and spelling them, its mass will diminish and the diminishing mass will lead to an increasing acceleration during the ascent.

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Of course, this is just an example and there are other factors to consider as well. For example, the diminishing, um, Eh, if force exercised by the atmosphere, uh, as a, if you go higher and higher altitudes there is less and less air and uh, as a consequence, eh, the rocket will have an easier and easier time, uh, ascending due to that too. Here is another example in this case of a variable rate of deceleration. Imagine you are in your car and you are going not too fast, but maybe just a little bit too fast and the pedestrian steps off the curb in front of you. And as you brake, you know the to stop and not to hit the pedestrian. After if you, um, seconds or a fraction of a second, you realize that you must break harder. And as you step on the brakes, your rate of deceleration increases in order to be able to stop in time. And this will bring your car to uh, Joel Ping haulk.

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As a matter of fact, in the various legal and contractual frameworks surrounding public transportation, there is a concept of jolt and jerk. Eh, jerk is a synonym of a jolt. It may even be more frequently used. If you look up jolt in a Wikipedia, it will automatically redirect you to the article. Talking about jerk. However, as I was designing this episode, I decided that I couldn't talk to you about a jerking technologies. So from now on we will be talking about Joel and Joel things. Technology's only, so when the bus, Eh, lurches and moves, uh, and the stops and starts, it is fundamental for the bus driver to drive as smoothly as possible rather than jolting and jerking and, uh, potentially causing damage to the passengers even without necessarily an accident. Because our skeleton and our muscles need to adapt, uh, to, to new speed or in this case to the new acceleration that must be reached.

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Very, very progressive. Lee. So I hope these two examples clarified the fact that when we are talking about jolt, it is a natural phenomenon that happens, that variable accelerations occur and we need to take them into account. In the series, I often talk about accelerating technological change in various areas. And as a matter of fact, the exponential technologies that, uh, we analyze and teach and singularity university, uh, for example, our Ed, the basis of so many things that happen in the world today that we must thrive to understand them, technology creates change. And over the course of centuries, if you observe this change, you may think that change is constant. But in reality, what happened is that almost imperceptibly the speed of technological change as it accumulates increases. That is why we are talking about accelerating technologies. And if at the beginning you couldn't realize that this is what was happening.

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After a while it becomes evident. And more and more people embrace the paradigm that there is an accelerating at a technological change and that it impacts business and society the way we live to the point where if for some reason in some area these acceleration doesn't keep up with our expectations, we are let down. We will say things like, oh my God, the new, um, mobile phone just announced, um, is not as big a change as we thought. It is. Just an incremental change. Incremental change doesn't satisfy our expectations anymore. So what happens with jolting technologies where the rate of acceleration itself is increasing? Do jolting technologies actually exist? Can we, uh, think about them? That jolting technologies will be here and they may influence, overwhelm, or really just disrupt what merrily accelerating technologies are doing?

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Okay.

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So if we think about the future of accelerating technologies that compared to linear phenomena, they may be incubating for a long time before becoming evident and disrupting the status quo. Actually with jolting technologies, these can be even longer or the impact of jolting technologies for quite some time can be even less discernible, even harder to um, latch on or to believe that that it is real. But then when the go beyond the given threshold, their effect is even more dramatic, even more. These rock gave even more explosive. So in order to convince you that jolting technologies are real, let me give you several examples that this is already happening in computation, communication, cognition, transportation, biology and elsewhere. Before giving you a those examples though, let me talk about a little bit more about the mathematical representation because it is going to be this mathematical representation that is going to lead to the model through which to analyze certain

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technologies and make falsifiable predictions that will demonstrate scientifically that the approach and the model are valid and useful.

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And that potentially will lead to applications in business applications in technology management than and so on. So the mathematical formula for accelerating technologies is the exponential function. For example, two to the power of X. And exponentials are often shown graphically as a line on a logarithmic chart for a given unit of time. The value measured and represented is going to increase in order of magnitude on a logarithmic chart. What we have is at the time typically represented, Eh, in, uh, the horizontal axis, one year, two years, three years, four years. And on the vertical axis we will have some kind of value. For example, very famously Morris law saying that the number of transistors that we can cram on a given area of an integrated circuit is going to double for, uh, every 18 months approximately. So on the vertical axis of an exponential, um, phenomenon as is represented on a logarithmic chart, we will have one 10, 100, 1000, 10,000 and so forth. And when you plot, uh,

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the transistor account of chips over the course of 20, 30, 40, 50 years, which is a, uh, the amount of time that we've known about more slaw, this self fulfilling prophecy, uh, of engineers worldwide. Trying to prove it true. You will find that we had deviations obviously statistically here and there, uh, but you will be able to correctly plot or interpolate, uh, the trips we did there, transistor densities and transistor accounts. Um, on a, on a line on this chart,

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okay.

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The mathematical formula for jolting technologies is super exponential, such as two to the second two x. And when we represent this graphically on a logarithmic chart, we will have an exponential curve.

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Okay.

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This is just a visual aid, but it is also a tool for verifying if certain hypotheses about Joel things technologies can be interpolated. And as a consequence, if we can make certain forecasts about their, of, uh, growth for the future, what would happen? What we would need to be able to observe is that for every unit of time, the value that we are predicting for jolting technology will increase and increasing value. Now, we are barely prepared to cope with exponential technologies. Famously, um, successful companies ran by extremely professional and well-prepared CEOs. We're lad to ignore competitors that add the time, didn't appear to be representing a menace to the leadership of that industry and unseating the leading company. But then they've proven to be formidable competitors pushed ahead and sustained by the exponential technologies that they were driving. And a former leaders could do practically nothing.

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And they succumbed and they were eliminated. The failure of Kodak, of uh, predicting the, um, success of digital cameras both technologically as well as the various use cases that would put them in the hands of billions of people. The failure of, uh, blockbuster predicting, uh, the success of Netflix, Eh, and that would, uh, lead to, uh, they are going bankrupt for both of these companies. That as a matter of fact, well if that is the case, I think it is safe to say that we are completely unprepared, desperately. I'm prepared to face the explosive changes that jolting technologies are going to bring. And the statistical variations of course that can happen where the various forces at play, uh, contribution to, um, these concrete examples, uh, that we are analyzing not to be exactly on those predicted paths of development. But being, uh, outperforming or underperforming them, uh, these variations are going to be, eh, really a dramatic if you hated the stock market flash crash that

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happened, the, um, uh, a decade ago or, or, or so, then wait until, uh, the world is going to be dominated by jolting technologies. But that is exactly why it is so urgent that we develop a series of methodologies that allow us to cope with the consequences of jolting technologies defining our world.

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Because the way that we understand the world will be profoundly impacted by them and the world itself. The reality on the ground. How far is our image already oftentimes of an African country and African city? How far is, uh, the image we have of China or, or of of other countries? How many New York curse driving, uh, the crumbling subway? They, Lee have experienced the size fiction grade subway in Beijing or Shanghai, just the tiny fraction. They don't realize how out of sync their worldview is from reality. And as Joel think technologies are going to be unevenly adopted in various parts of the world. There will be truly areas that embrace science fiction fully and other areas of the planet that stay behind, maybe not even realizing or uncomprehending and these believing because the changes are not just going to be of a trash field, smelly, crumbling subway, uh, in the world finance capital that is so proud of its role compared to a shiny, reliable, fast, uh, system on the other side

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of the world.

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But the differences will be even more dramatic of what the these going to be to be human. What is the biological basis of being human? What are the applications of AI or nanotechnology that society is able to support and to absorb? Who is going to make our species interplanetary? Who is going to explore the, so let me give you in closing, if you rapid series of jolting technologies as examples to show you that this is real, this is happening, quantum computing and you will say, well where, where, where is come quantum computing and you will be right. Quantum computing is not still here, but that is kind of exactly the point. The first time I used quantum computers, and this deserves a, of course as a technology, but also as a anecdote, a separate episode of the context was 10 years ago. And it's not that things have been dramatically transformed since, but joking technologies incubating perceptively for a long time, a longer time than merrily exponential technologies and they will be

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more dramatic.

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Why is this the case with quantum computers? Because the components of quantum computers obey more slaw. But the way that quantum computers are able to leverage their components is exponential. So there is an exponential substrate supporting exponential software and that is why the consequence of the combined effect is super exponential. Second example, gene sequencing, we have been showing, I have been showing in my conferences charts of the decreasing cost of gene sequencing over the course of many years. And this chart would show a, a line in a logarithmic chart showing that, uh, rather than, um, $3 billion, uh, 15 years ago, the cost of a, um, complete gene sequencing, uh, is 3 million and then 3000. Not even that. It rather than the decrees being merrily exponential, the decrees is super exponential. The decrees is jolting in the last few years. Quite interestingly, this Xceleration in the power of GC gene sequencing and the deceleration, uh, in the cost, the dramatically

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dropping cost actually jolting acceleration and jolting deceleration in the costs slowed down.

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Now it is merrily exponential. And the reason is because the patents in the gene sequencing machines are about to expire. So Radha Dan respecting their clients and respecting the market forces, the holders of those patents are seeing, oh my God, this is a, going to these appear. We are not going to be able to extract from the market these juicy, uh, royalties and these juicy charges that we have been doing in the past several years. So let's milk it for these last few years. Let's milk it as much as we can. Even if with these, these respect for the market forces, probably we are Aliya nading uh, the ecosystem participants that realize that we are taking advantage of them. It doesn't matter because now we are protagonists, we are leaders, but we are not going to be anymore when our patents expire. Everybody else who's going to jump into the market. So let's ride it out on field. The going is good.

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After these two examples of concrete technologies that Eh, represent Joel think technologies today. Let me give you a few predictions. What I expect of other technologies to have these jolting characteristics. One of them is in communications where the deployment of generation after generation after generation of communication technologies brought in more and more people. And each of these has observed a, an exponential growth. Uh, each of them has been able to capture more and more orders of magnitude of people. But then, but the, um, interpolation, uh, the cumulative, uh, chart that you can design of the generations of communications technologies is designing a super exponential and the next generation to fifth g, uh, networks, five g networks as well as the simultaneous deployment. Uh, low earth orbit, uh, swamps of satellites for communications that space x and Google and Facebook and many others are simultaneously trying to, um, to, uh, both launch as well as thought, uh,

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operating in the next, the a handful of years is going to represent that, uh, Joel thing disruption, uh, as, uh, there will be at the launch of options of gigabit per second and more, uh, communication options for everybody, uh, on the planet and other, um, um, technology that I expect we'll have this kind of jolting effect, uh, ease the deployment of self-driving electric cars, Eh, after, uh, the, um, combustion engines success over the course of the 20th sanctuary.

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Uh, weed. Now battery technology in computing technology, being able to, uh, deliver the performance that we want. Uh, a real paradigm shift is going to happen and it is going to have a jolting effect on the industry. What is going to be this jolting effect in my prediction, every car company that is not already, uh, fully believing and already investing tens of billions of dollars in the development of sales driving electric cars is going to go bankrupt.

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Last example, artificial intelligence, neural networks, deep learning and other technologies being applied not only to real world challenges, which is absolutely fine and necessary, but artificial intelligence, designing AI systems with these various approaches and others that will emerge. And of course quantum computing also having to do with all of it, the lurking of AI performance that has been quiet dramatically in the news, uh, because of its exponential power is going to shift into this jolting metaphor and AI systems being able to design AI systems without at all that necessity of it becoming conscious and wanting to kill humanity or pretending to have human rights or being able to solve universal problems, uh, under the artificial general intelligence paradigm without all of that is going to already represent the Joel King Technology. Weird, huge disrupting force.

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Okay.

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Do we need all of these predictions to come true? Do we need all of these technologies to actually obey my expectation of, uh, this new metaphor, this new approach, this new methodology needed to understand them? Doesn't matter. It doesn't matter if some of them will be merrily exponential, Eh, and not all of them proving to be jolting technologies, but as some of them will be, our world is going to be transformed. Now you may think, actually let's pretend I'm joking, not about what I just said, but what about what I am about to say right now? Let's pretend I'm joking because John Zinc technologies are the ones with a variable rate of jolt John Technologies and I'm not gonna cover them anymore in this episode of the context, our naturally past singularitarian, we need advanced jerking and jolting technologies to understand them. Should we care about the power of Joel's saying technologies today? Maybe, but if that is true, it will be for a new episode of the context. So thank you very

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much for listening and watching this until the end. I truly appreciate your attention and if you want, you can become a supporter on pantry on and help me and my team with as little as $5 a month or even less if you find the option because a, that is just a suggested amount and you can become a supporter with any amount to produce more episodes of the context and to create the future together.

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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:

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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.
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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:
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Main Topical Clusters:

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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:
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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:
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The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.


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Action Advice:
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Structural Gap
(ask a research question that would link these two topics):
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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):
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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
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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
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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


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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:

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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):

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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:
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Network Structure Insights
 
mind-viral immunity:
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stucture:
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The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its 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.

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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:
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(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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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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