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InfraNodus
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Welcome to this new episode of the context. My Name is David Orban and I want to talk to you today about human networks. We have of course, formed networks in our societies since he existed. We are social animals. At the beginning, these networks were our villages, our dwellings, and they were comprised of a few dozen, maybe a few hundred individuals. And we were a very unlikely to actually go outside of the circle. And it was only eh, narratives of fairy tales and our myths that would permeate back and forth with the rhythms of a slower life that would change what we would be thinking, how we would be thinking about the world. It was a deep time linking cultures probably across continents, across the entire planet, but through generations. Some of these myths, some of these narratives, some of these networks survive to these day. When you read the Odyssey or the Iliad, you are participating in the written communications ability to actually extend the network a from a completely

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location constrained interaction to one that is able to link people who are not at the same place together.

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And of course written communication. Made it possible to record once memories transmit was so it's, but it also enabled us to reply. We started to have these networks that were intertwined with correspondence. One of my favorite ones is the one between Leibniz and Newton as a matter of fact, between Leibniz and Clarke a strong supporter of Newton to talk about their respective discoveries in mathematics and as they applied to, to physics. But Eh, what Dan became a broader conversation around what these the meaning of life and the respective philosophies and theologies that were shaping their understanding of the world. The typology of these networks as they developed in the past were of course, in my view, an expression not only of the nature of human social interaction, but very strongly also of the technologies that that were available.

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The most important example I would say is how we organize the, our cities the kingdoms, the city states of the ancient world and within the city states, the case, the system or the case like systems. And within those systems, the communication between the individuals and the command and control mechanisms that were available at the time. Most of these typologies were extremely hierarchical. The military organizations have how the rankings would enable or theirs to be formulated, promulgated and communicated, implemented and who would outrank whom. The technology of course changes and eh, the one to many communication channels that even up to the 20th century characterized communication such as books we try already mentioned, but also radio, television, cinema where everybody participating in receiving the message that these very hierarchical top down networks were able to, to promote, could have almost no opportunity to, to speak back.

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Well, these communication technologies in the 21st century have been substituted by many to many communication platforms and our networks, our interactions as a consequence profoundly changed with them. Once again, I would say that an important and interesting example of these changes, how even the most hierarchical, the most regimented network, the military organizations are now recognizing how a different kind of more peer to peer network topology can be desirable in mission driven tasks where the orders are not top down anymore. But there are multiple input sources and a more, not necessarily eagerly Tarion, but a multifaceted constitute ration for the decision making process that gets into, into their thinking and their actions as well. So these many to many communication platforms, Eh, are important because they represent a multiplying force to what used to be the village where everybody would know everybody else and gossip would drive the accumulation and distribution of

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knowledge in, in waves about the, the various types of behavior that were admissible or inadmissible ambitions that would be thwarted or supported extreme and measures of ostracism or expulsion that were necessary to, on one hand allow individuals to escape from the limits of their, Eh, imposed social structure.

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But on the other hand, to preserve the identity of a, the social structure as well. Some of these new platforms like email are now 50 years old and email is still one of the more sophisticated means of asynchronous communication. Synchronous or asynchronous means that the various parties taking part in the communication need to be simultaneously together. Synchronous communication or the various parties can afford to absorb the message and respond to the message at their own pace. And the communication platform supports these kind of alternative, a system of interacting asynchronous communication. So email is asynchronous and and the modern email clients allow threading for example, where the subject line of the email will group the various replies set together and anybody listening to this check if your email client allows a threaded communications and I strongly recommend that you turn on the threads.

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And then when you start to you to like that kind of grouping which may compact, defy the list of various emails, not necessarily in a confusing you with the the lease that becomes too long. And, and something that you didn't reply to goes out of your main screen and you forget about it. That is when you realize that you should change the subject line of an email consciously knowing when it is time to do that, knowing when you should keep the original subject. And it is typically because the topic stays the same or the topic actually changed and it deserves a new threaded conversation on email. There are many other reasons why email is still very robust and very rich in, in, in features that other, more recent platforms haven't had the time of, of accruing yet.

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The most famous synchronous communication system of course is a telephone even though originally it only used to be one to one. And then a phone conferencing systems emerged and eh, they were the bane of office management where a lot of times and steely these, the k's a conference, Cole is a lost opportunity because of the way that it is often organized or ms organized. Sometimes it is too long. Oftentimes the participants don't establish what the actions are. At the end of the call a m it is, it is really, Eh, in a large percentage and not very useful at all.

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Internet relay chat, IRC is another example of a fairly ancient electronic chat system that a, a lot of people still use. I'm not the hundreds of millions of people that use we chat or whatsapp, but still millions of people maybe tens of millions of people. WHATSAPP in particular is very popular in the West. We chat these extraordinarily popular in, in China and while we chat became a platform in its own where additional modules allow hiring a taxi cab or booking a restaurant, making a payment and many, many other things. Watsapp remained mostly a, a chat platform and rather primitive as that one of the most important features of text, that voice is very slowly taught, starting to inherit due to AI voice and speech recognition systems is that taxes searchable. And that is why for me, email is still so useful because it allows me to, to search in a sophisticated manner. And find messages that are relevant to what I want to you do. And chat systems, whether we are talking about Skype,

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telegram, signal, whatsapp or utters haven't achieved those levels of sophistication in their features, either a searching or archiving, organizing, threading, et Cetera, et cetera.

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Nonetheless, of course, these systems are sophisticated enough to develop kinds of behavior that others can judge is appropriate or inappropriate for the platform in the physical world, that these kinds of interactions are called the etiquette of a well behaved conversation. Or I'm visiting somebody or having dinner and using the right kind of cutlery with the right kind of food and other sources of embarrassment, Eh, more or less purposefully designed in order to artificially distinguish and sort people in those who are well versed in that these arcane practices and those who are newcomers or who just snuck in but don't necessarily belonged the corresponding rules of behavior in the electronic systems that weaved a human networks, Eh, is Nneka. And of course, netiquette often re implements reinvents these rules. Why not for similar purposes of labeling, sorting, classifying, admitting, banning individuals in bought, dissipating get certain degrees in these networks and

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The electronic platforms that gave rise to what today we call social networks from friends there to Facebook in the, in the middle with the, with so many others express powerful lessons about how important topology architecture, interaction, netcat and features actually are. We have learned last year one of these powerful lessons when for the umpteenth time, Google threw in the towel after Google wave googled Barraza Google items. Remember even how many other attempts at building the social network native to Google, they threw in the towel on Google. Plus at the time, there must have been [inaudible] attempt of building a social network at which they completely failed. More recently they threw in the towel on and honors social feature that they tried to build in to youtube. I don't know if you even knew, but on top of the commenting system that is sitting beneath that every youtube video that allows the viewers to express their opinions often very negative, sometimes positive, and

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that the creators to respond, but also viewers having conversations.

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Youtube introduced a feature that they were supposed to be extending for a community type of a section. And if you didn't know, don't butter learning because they already closed it. Isn't it fascinating that money is not enough? Google could have all the amounts of money they want to dedicate to these tasks. Millions of dollars, hundreds of millions of dollars, billions of dollars, maybe even tens of billions of dollars. Wouldn't it be worth it if they could quotation marks, beat Facebook, but it money is not enough. They'd, that is a huge lesson for startups that are jealous of their ideas. Isn't everything that Facebook does available for others to copy or be inspired by as a Google could have been inspired and was for sure in designing the Google plus the d d the barrier is not evil. The idea is known or correct rudder and others cannot copied. The barrier is not not having money because we've seen that having money's not enough. The ability to build something that people want and,

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and really use day in, day out, the ability to create features that in this case create new important social networks is what makes a big difference.

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Today, today to get her with Facebook and other important social network for human networks on the professional segment is linkedin and Linkedin has been acquired by Microsoft for $14 billion a few years ago. And some including me, expected that the acquisition would signal the excessive integration of linkedin into the Microsoft ecosystem, such as requiring the use of the Microsoft account for signing in or signing up for Linkedin or, or other mistakes. But it wasn't the case. And linkedin is evolving adding new features like the ability today of in signaling interest in a post or exclamation or the traditional thumbs up, liking, inspired by Facebook's similar nuanced expression of a feedback in a post live streaming of a video. And the, the ever more sophisticated creation of content both in groups as well as on the individual profiles

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On Linkedin. There are a lot of people who are maybe as a consequence of these new features that are being added, confused what they should be doing with the platform, how active should they be in those kinds of traditional social media sharing activities that we got accustomed to on on Facebook. Should you share cat videos on linkedin? Should you stream your life of what you are eating or your influencer type photos on Linkedin? What a more traditionally belonged to a telegram. The experiments of course are endearing because sometimes it is very easy to see that they are blunders and no you should not share cat videos on, on Linkedin, but it is positive too to have experiments. Nonetheless. One of the things that I have been a watching and I want to experiment with myself is not the only live video. Which Eh, I am definitely going to to do probably on twitch, but the re streaming of video across multiple platforms where then you can gauge which platform is more amenable to these live

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activities and, and which ones you should invest more.

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It is also fascinating to look at what are the limits of the platforms. The most striking limit for example on Facebook is that you cannot add the more than 5,000 friends connections. And I used to think that this was a mistake may be that a Facebook should allow a larger number. As a matter of fact on linkedin. The limit is 30,000. Why shouldn't Facebook go as far the traditional measure of connection limits? The Dunbar number which is set at 150 is our supposed ability to have strong connections to other people and our origins, seeing tightly knit communities biologically evolved this limit to be that low, relatively speaking. But there is also a concept of really making the connection valuable. It doesn't matter what tools support the connection, it could in increase the Dunbar number, let's say from 150 in a physical world of a village to 5,000 on a Facebook and to 30,000 on Linkedin and maybe to a, an even larger number with other support tools and other platforms.

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But still that network is yours and it is not just a, a mathematical and a topological concept. The value of that network is in your investment, your curation, your interaction the ability to introduce somebody to somebody else when you asked, Eh, and how today's practices of double often in these introductions allow the other party to say thank you. I don't want to meet your friend today on email or in person. And the favor that a he's asking is a burden to me and please do not complete the introduction. They say is rather important. Double Opportunities, a precious tool for a smooth thing. Otherwise embarrassing situations. Well, I don't like as a consequence, those linkedin users that have Lyon written on after their name, these started as a kind of a protest the way in a linkedin started re restricting the, the way that you could use your network.

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And in lion stands for linkedin open network or, or network or people who will accept every connection. And for me that means that you become like a stamp collector. You are not applying any judgment to who you should or shouldn't accept. And the value of the network is severely degraded as a consequence. Now, obviously the value of the human networks is not only in professional or social support or in our emotional ability to share what we do. It is also in our ability to follow through to act and Vodi. Allen used to say 80% of life is showing up. So following through in a human network in a social network is really important. Actually 80% or more, whatever the number is going to be accomplished just by delivering what you can deliver and what you said you will deliver in a social interaction already.

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Sophisticated tools are supporting our human networks in many linkedin. Again, when you start the conversation, you are given suggestions on how to open it, how to continuate and these pieces of AI support in the enriching of human networks are going to get ever more sophisticated. We will talk about synthetic persons and the AI based personas. These will be more and more important. These are going to be part of our future human networks. This will be for another episode of the context.

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I want to thank you for having watched this episode and I want to invite you to become a supporter on Patreon if you liked it. This allows me for as little as $5 a month on your part or the amount that you choose, less or more, to keep producing these videos together with my team, who helped me designing, researching, editing, publishing, and promoting on the various human social networks, the videos of the series of The Context. Thank you very much.

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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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Main Topical Groups:

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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:
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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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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


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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...
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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 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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