graph view:
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Graph Language Processing Settings:

 
Specify the settings for your text-to-network conversion algorithm for this graph.
Lemmatizer: ?
Every word will be converted to its lemma (e.g. bricks > brick, taken > take) and will be shown on the graph as a node. Set to your language for more precise results. Switch off to turn off lemmatization and add your custom stop words list below.
 
Show on Graph:   Double Brackets [[]]:  Categories and Tags:   
Stop Words: ?
List the words, comma-separated (no spaces), that should not appear in the graph, in addition to your default global stopwords list.
Example: is,the,as,to,in

 
Synonym Nodes: ? unmerge all
If you'd like some words to appear as one node on the graph, in addition to your default global synonyms list, list the synonyms, one per line.
Example:
machine:machine learning
learning:machine learning

 

×  ⁝⁝ 
Dynamic Graph Settings


See the dynamic evolution of this graph: scroll or "play" the text entries to see how the text propagated through the network graph over time.

the final graph

highlight propagation edge
show visible statements only



 
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current speed of the player:
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one statement at a time


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Export the Data


Network Graph Images:

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

Visible Statements (Tagged):

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

Network Graph Data:

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

All the Text:

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

#infranodus is different from #rhizi because it can do text network analysis. however, rhizi allows real time data visualization. both projects are opensource.

   edit   deselect   + to AI

 

#infranodus is different from #cograph because it can also do text network analysis and can process natural language statements to build graphs from them. it also has import functionality. also cograph is not opensource yet.

   edit   deselect   + to AI

 

#mohiomap visualizes connections between evernote notebooks, while #infranodus can visualize the actual relations between the notes, revealing the recurrent topics and the most relevant words.

   edit   deselect   + to AI

 

#metamaps is more of a mindmapping tool but it can also be used for sharing graphs like #cograph

   edit   deselect   + to AI

 

both #metamaps and #rhizi offer real-time collaboration on graphs

   edit   deselect   + to AI

 

#quid offers data visualization for interconnected data like #cograph does

   edit   deselect   + to AI

 

#quid is a specialized network visualization app while #infranodus has a more general application range

   edit   deselect   + to AI

 

#discovr shows connections between similar artists, like #wikiweb does for wikipedia

   edit   deselect   + to AI

 

#mohiomap visualizes #evernote notepads

   edit   deselect   + to AI

 

#infranodus visualizes connections between #evernote notes

   edit   deselect   + to AI

 

#infranodus runs on #sigmajs and #neo4j

   edit   deselect   + to AI

 

#linkurious visualizes #neo4j databases and uses their own version of #sigmajs

   edit   deselect   + to AI

 

#infranodus has #twitter import

   edit   deselect   + to AI

 

#quid can visualize #twitter data

   edit   deselect   + to AI

 

#wikiweb can show connections between wikipedia articles, like #rhizi but is not editable

   edit   deselect   + to AI

 

#cograph is a tool for sharing information in the graph http://cograph.co

   edit   deselect   + to AI

 

#rhizi is a new type of network graph documents and a collaboration tool http://rhizi.org

   edit   deselect   + to AI

 

#linkurious is a visualization tool for neo4j databases http://linkurio.us

   edit   deselect   + to AI

 

#sigmajs is a graph visualization library perfect for sharing graphs http://sigmajs.org

   edit   deselect   + to AI

 

#discovr can show connections between artists like #infranodus https://itunes.apple.com/us/app/discovr-discover-new-music/id412768094?mt=8

   edit   deselect   + to AI

 

#wikiweb app can be used to discover connections between wikipedia articles (not editable), a similar feature but for google search results is available in #infranodus and it's editable http://www.wikiwebapp.com/

   edit   deselect   + to AI

 

#metamaps is a beautifully designed collaborative mindmapping tool http://metamaps.cc

   edit   deselect   + to AI

 

#infranodus is a tool to retain and share knowledge as a network http://infranodus.com

   edit   deselect   + to AI

 

#synapp is an organizational network analysis tool, which helps organizations to map out their social networks and for the members of these organizations to estimate their position in the network http://www.seeyournetwork.com/

   edit   deselect   + to AI

 

#infranodus can be used for organizational network analysis but #synapp is made especially for that

   edit   deselect   + to AI

 

both #synapp and #rhizi can be used for organizational network analysis, but #synapp is more specialized

   edit   deselect   + to AI

 

#dna7 is provides organizational and leadership diagnostic solutions using network analysis http://dna-7.com/

   edit   deselect   + to AI

 

#dna7 and #synapp offer similar service

   edit   deselect   + to AI

 

#socilyzer offers social and organizational network analysis https://socilyzer.com/

   edit   deselect   + to AI

 

#socilyzer offers similar service to #synapp and #dna7

   edit   deselect   + to AI

 

#polinode offers organizational network analysis SaaS https://www.polinode.com/

   edit   deselect   + to AI

 

#polinode is a bit like #synapp

   edit   deselect   + to AI

 

#polinode runs on #sigmajs

   edit   deselect   + to AI

 

#linkfluence is a SaS for studying social network activity of a brand - it can visualize #twitter data

   edit   deselect   + to AI

 

#quid is a bit like #linkfluence

   edit   deselect   + to AI

 

#graphcommons is a project that lets people share networked data as graphs

   edit   deselect   + to AI

 

#rhizi is similar to #graphcommons

   edit   deselect   + to AI

 

#cograph is similar to #graphcommons

   edit   deselect   + to AI

 

#infranodus is similar to #graphcommons but it has a text interface

   edit   deselect   + to AI

 

#quid is a bit like #palantir_technologies but more focused on using networks for insights

   edit   deselect   + to AI

 

#keylines is similar to #quid

   edit   deselect   + to AI

 

#keylines works on #neo4j platform

   edit   deselect   + to AI

 

#moviegalaxies uses #sigmajs to visualize connections between characters in movies

   edit   deselect   + to AI

 

#textexture is a text network visualization app, its algorithm is used by #infranodus

   edit   deselect   + to AI

 

#textexture runs on #sigmajs

   edit   deselect   + to AI

 

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Total Nodes Shown:
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semantic variability:
×  ⁝⁝ 
×  ⁝⁝ 
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  
×  ⁝⁝ 
     
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):
loading...
 ?  

LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):
loading...

We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

Sentiment Analysis


positive: | negative: | neutral:
reset filter    ?  

We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

The approach is based on AFINN and Emoji Sentiment Ranking

 
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.

Keyword Relations Analysis:

please, select the node(s) on the graph see their connections...
+   ⤓ download CSV   ?

Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
Top Relations / Bigrams
(both directions):

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. 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
0
0
0
0
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Network Structure Insights
 
mind-viral immunity:
N/A
  ?
stucture:
N/A
  ?
The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

Modularity
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Influence Distribution
0
%
Topics Nodes in Top Topic Components Nodes in Top Comp
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%
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0
%
Nodes Av Degree Density Weighed Betweenness
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Narrative Influence Propagation:
  ?
The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

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

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

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

   advanced settings    add data manually
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):

     advanced settings    add data manually

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

 advanced settings    add data manually

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