InfraNodus -

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.

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The graph images for publishing on the web or in a journal. For embeds and URLs use the share menu.

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Visible Statements (Tagged):

Export the currently filtered (visible) statements with all the meta-data tags (topics, sentiment).

CSV (Spreadsheet) MD (e.g.Obsidian)

Text Mining Analytics:

Summary of insights from the analytics panel. For specific in-depth exports, see the download button in the Analytics panel.

TXT Analytics Report CSV Report N-Grams CSV

Network Graph Data:

The raw data with all the statistics for further analysis in another software.

JSON CSV Gexf (Gephi)

All the Text (for backup and duplicating):

Plain text used to create this graph without any meta-data

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Your Project Notes

Interpret graph data, save ideas, AI content, and analytics reports.

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.

[] [rhumbl]

@rhumbl allows to visualize any #excel or #csv spreadsheet as a network graph https://rhumbl.com/

15647790590000000

[] [vis]

@vis allows to create networks but its functionality is based more on #mind_maps it takes #excel data as input https://vis.occrp.org

15647797190000000

[] [databasic]

#connect_the_dots is a project by @databasic which can visualize any #csv or #excel data as a network and export as #gefx or #png

15647801180000000

[] [wtfcsv]

@wtfcsv can be used to visualize #csv and #excel as a #tag_cloud

15647802900000000

[] [kumu]

@kumu is a tool that can be used to create network #maps of #concepts and to showcase their relationships along with a complimentary #text https://kumu.io

15647806550000000

[] [infranodus]

@infranodus can be used to visualize #text, #csv, #excel and can export data in #csv and #gexf format

15647807130000000

#kumu basically creates #mind_maps

15647807810000000

[] [linkurious]

@linkurious can visualize #neo4j data and can also be used to create maps https://linkurio.us

15647822020000000

#infranodus works on #neo4j

15647822100000000

#linkurious is like a more technical version of #kumu and has more features: it allows to perform the basic #network_analysis calculations

15647822650000000

[] [infranodus]

@infranodus performs #network_analysis on the data to detect the most important relationships, hubs, and #structural properties of the relations

15647823410000000

[] [graph_commons]

@graph_commons can visualize #concepts and be used to #mind_maps. It also provides basic #structural information and rudimentary #network_analysis https://graphcommons.com

15647830910000000

[] [mindnode]

@mindnode can be used to create #mind_maps https://mindnode.com

15647833640000000

[] [keylines]

@keylines can be used to perform #network_analysis and better understand the #structural properties of a system of relationships https://cambridge-intelligence.com/keylines

15647834620000000

[] [greaterthanthesum]

@greaterthanthesum makes software that can be used to create #maps and networks of #concepts

15647839780000000

[] [fluorish]

@fluorish can be used to visualize #concepts from #csv and #excel and their relations and the basic #network_analysis https://flourish.studio

15647851240000000

[] [graphonline]

@graphonline helps create graphs and provides basic #network_analysis features (eg shortest path) https://graphonline.ru

15648297720000000

[] [graphistry]

@graphistry can visualize network data from #neo4j or #csv sources and can be used for #storytelling https://www.graphistry.com/

15648299380000000

#kumu can be used for #storytelling

15648299490000000

[] [yworks]

@yworks is a graph visualization suite that can be used to build graphs and to get interesting insights about the #structural properties (it takes #csv and #excel data as import)

15648348220000000

[] [polinode]

@polinode offers organizational network analysis SaaS and can perform #network_analysis and get insights about the #structural properties of a network https://www.polinode.com/

15648350140000000

[] [quid]

@quid is a platform to get insights in data using #network_analysis http://quid.com

15648350340000000

[] [arcade_analytics]

@Arcade_Analytics can do #network_analysis and is best suited for #storytelling

15771208750000000

#graph_commons is best suited for #storytelling using the graphs

15771208970000000

[] [cytoscape]

@cytoscape performs #network_analysis and #network_visualization

15771245350000000

#infranodus is a #network_visualization tool

15771245460000000

#arcade_analytics can visualise #neo4j data

15771245650000000

[] [gephi]

@gephi is one of the best #network_analysis and #network_visualization tools

15771246370000000

#networkrepository is a tool for #network_visualization mainly used in science

15771249890000000

#infranodus can visualise a network of #concepts

15771250180000000

#infranodus can be used to create #mind_maps

15771250350000000

[] [lynksoft]

@lynksoft can be used to build #mind_maps and #network_visualization and perform #network_analysis on the connected data

15771251930000000

[] [nodexl]

@nodexl is an #excel tool for #network_analysis

15771257950000000

[] [rhumbl]

@rhumbl can be used to map #concepts and #mind_maps

15771290970000000

[] [rawgraphs]

@rawgraphs #mind_maps #network_visualization https://rawgraphs.io/

15873746350000000

[gephi]

@gephi is a super powerful tool for #network_visualization and #network_analysis. It can process almost any data, including #csv and is great for #large_graphs.

16066749270000000

#gephi and #yworks are two very solid products but #yworks is more focused on #diagramming while gephi is more focused on #network_analysis

16066750110000000

[sigma_js]

@sigma_js is a #javascript library for #network_visualization

16066750370000000

[graphology]

@graphology is a library for #network_analysis and works well with #sigma_js

16066750490000000

A lot of #linkurious was written on #sigma_js

16066750570000000

#infranodus uses #graphology and #sigma_js for #network_visualization and #network_analysis

16066750740000000

#graph_commons can be used for #social_network_analysis

16739005140000000

#infranodus can be used for #social_network_analysis

16739005240000000

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Semantic Variability Score

— modulates diversity of the discourse network how it works?

The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).

Actionable Insight:

N/A

We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).

1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).

Read more in the cognitive variability help article.

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:

Main Topical Clusters & High-Level Ideas

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.

please, add your data to display the stats...

Most Influential Keywords & Concepts

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.

We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

please, add your data to display the stats...

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.

Download: TXT Report CSV Report More Options

Structural Gap Insight

(topics to connect)

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.

N/A

Discourse Entrance Points

(concepts with the highest influence / frequency ratio)

These nodes have unusually high rate of influence (betweenness centrality) to their frequency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.

N/A

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.

Concept Relation Analysis:

please, select the node(s) on the graph or in the table below to see their connections...

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 in 4-grams

(bidirectional, for directional bigrams see the CSV table below):

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:

Discourse Network Structure Insights

mind-viral immunity:

N/A

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.

We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.

The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.

Modularity	0	Influence Distribution	0 %

Topics	Nodes in Top Topic	Components	Nodes in Top Comp
0	0 %	0	0 %
Nodes	Av Degree	Density	Weighed Betweenness
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Discourse Advice:

N/A

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)

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:

(based on kolmogorov-smirnov test)

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.