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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.
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There is an explosion of interest around the concept of the metaverse, and it is worth describing its various features the parameters that can define how different people may interpret the metaverse for their own purposes. Today, I will talk about the hardware and the software that can support it. The new business models that can be really disrupting, even towards the existing leaders of technology. And finally, how one of our favorite concepts Artificial Intelligence has an important role to play. My name is David Orban, and this is the context.

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We have many

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different words that from time to time we employ in order to describe our concepts of a digital reality. It can be the matrix, it can be virtual reality and augmented reality. It can be the metaverse, and many others too. For the past several months, there has been an increasing level of interest around this set of concepts. And in particular, using the name metaverse. I am talking about digital realities. Because for me, describing implementing and leveraging what we can do in the digital world is not inferior, it is not less real than what we can do in the physical world. We have had millions and billions of years to adapt and to understand, to perceive and to act on the physical world, through our bodies and our senses. So it is not surprising that the equivalent abilities for the digital realities will need some time to develop as well. We have only had a few decades to develop them. And we have to be a little bit patient, even if we are very excited and eager to start exploring

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right away. Today, we have advanced visors, for example, the Oculus that we can use in order to perceive a digital reality in three dimensions, a and there are versions of these visors that are able to mix digital and physical, it appropriately enhancing our description and our perception of the physical world through these digital overlays in augmented reality, whether virtual reality, which I don't like but there you go, or augmented reality headsets, these are all today very heavy. And this single factor makes it so that only very enthusiastic users want to wear them. And even they only can do so for let's say one or two hours per day and not more. This also means that, for the time being the vast majority of our activities are not going to be mediated by these immersive visors.

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It is up to the software layer to deliver the benefits of the metaverse independently of the type of interface that we are using for the past 10 years and more. A lot of people have been very active in Second Life, for example, which is a three dimensional digital world where the users have very powerful tools to create within the world and also trade and transfer their creations. But they have been doing so without the immersive 3d visors. They have been using traditional monitors and the mice. Their experience wasn't inferior, the passion and the value that they were creating. was worth it. Now, whenever we are using visors or monitors or maybe our mobile phones, it is fundamentally important to understand what is the reason we are there. For video games, it is unquestionably exciting and extremely empowering to be able to be immersed as we shoot and kill aliens or zombies or play ping pong may be with someone who is 1000s of miles away, and and other fun activities that we can now

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do in the metaverse. For business activities, it will be crucially important not to pretend that the objective is just to have a better zoom call meetings. No, that is not going to be the killer app of the metaverse in a business environment. Now, the solution is hard. We know for example, that $4 billion are not enough. That is the amount of money that magically braised originally to develop a consumer level visor. And then they changed their track they pivoted and now they are going to build vertical applications for surgery for training and other corporate oriented applications. And it is questionable, whether they will ever be able to recoup the investment with the necessary multiple that it is expected from them. Is it going to be $10 billion, or 20? We don't know yet. But the hardware is expected to become over the years ever more evolved, integrated in smart glasses. And maybe in many years later, for those of us who are adventurous enough in the brain implants that are going

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to be able to stimulate directly our visual cortex in order to make us see the digital reality.

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Whatever is the application, a new business model now is available. And it is based on NF Ts. Regardless of the Conklin, clunkiness of the acronym, non fungible tokens, NF T's are here to stay, they implement a unique traceability for digital objects. Imagine if in the physical world, whatever I put somewhere, couldn't be found next day. Well, that happens with our socks, right? But actually, every object in the physical world is always perfectly traced, is never lost. Well, in the digital reality, we now have the same ability, actually even superior because in the physical world, we do not have access to the history to the provenance to the origin of all the objects. And now in the digital reality, we have that and all the properties, all the evolution, the ability to be transferred, copied or not copied of these objects can now be embodied in their characteristics as NF Ts. And of course, NF T's themselves are based on Blockchain, globally distributed and decentralized networks that

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are becoming ever more powerful and ever more important than the impossible to ignore. The new business model is important because it allows companies to completely sidestep and overcome potentially disrupt what has made companies like Google and Facebook into the powerhouses they are today. Their business models are based on selling advertising, much better than it could be done ever on paper, but still something very mundane. And the ability to do without the advertising business model is going to be A crucial component of how the metaverse thanks to MFDs will evolve in the future. The last point I want to make is regarding the usefulness of representing digital realities that are different from the physical world but have connections with it. In Internet of Things, applications, or industry 4.0, as it is called in Europe, we have concepts of digital twins, representations of machinery, or industrial plants, all kinds of complex physical objects that acquire a new flexible way of

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being looked at when they are represented digitally. This is much more than just looking at a computer aided design file with a sophisticated visualization, because we can start asking questions, what would happen if I made this kind of change, or that kind of change around this model. And as we learn which changes are beneficial, and with which changes instead would be harmful for our objectives, we then can decide how to implement in the physical world. What we learn about these sophisticated models.

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AI platforms enable tremendous acceleration of this type of learning. Nvidia has platforms that you can already start understanding, analyzing and adapting so that these simulations can benefit your business goals. And companies like Tesla and SpaceX are already employing them in order to accelerate the evolution of their electric cars, where the parts can be changed and adapted as the models evolve, rather than once or twice a year, as it happens with traditional carmakers every day with new components being integrated in the production process, or starship, where you can see actually the company tearing down half built spaceship, because they learned in the meantime, from simulations that the given approach is better than what they have been partially executing on. And of course, they are courageous enough to adapt it, even if it means that they have to retrace some of their steps. This age aisle approach to hardware is going to be an important differentiating factor for future

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initiatives, business models, all M powered by a deep understanding of the implications of the metaverse.

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discourse structure:
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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:
  calculate & show   ?
(based on kolmogorov-smirnov test) ?   switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

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

Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
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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...
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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 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):
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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
(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 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
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Text Network Statistics:
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