InfraNodus -

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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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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.

#gianni_colombo exhibits at #greene_naftali_gallery

14277636830000000

#gianni_colombo exhibits at #greene_naftali_gallery http://www.greenenaftaligallery.com/artists/gianni-colombo#16

14277637160000000

#bernadette_corp is at #greene_naftali_gallery http://www.greenenaftaligallery.com/artists/bernadette-corporation#20

14277645470000000

#bernadette_corp has a similar aesthetics to #aes_group - both amplify the corporate and neoliberal aesthetics in its expression of glossiness and suspense of movement.

14277648200000000

#aes_group is with #triumph_gallery

14277648400000000

#harun_farocki is exhibited at #greene_naftali_gallery

14277652670000000

#turi_simeti is with #almine_rech_gallery

14277661050000000

#almine_rech_gallery also features works of #james_turrell

14277661320000000

#james_turrell light pyramids

14277661450000000

#jeremy_hutchison was exhibited at #modern_art_oxford

14278349920000000

#jeremy_hutchison was exhibited at #z33 in belgium

14278350070000000

#jeremy_hutchison worked with #delfina_foundation

14278353360000000

#jeremy_hutchison exhibited at #Northern_Gallery for contemporary art

14278354420000000

#jeremy_hutchison like #francis_alys lives in mexico and likes to make his work with everyday life situations.

14278355230000000

#aleksey_taruts is with #triumph_gallery

14278365280000000

#compagnie_marie_chouinard #les_ballets_c_de_la_b

14278368430000000

#compagnie_marie_chouinard #wayne_mcgregor

14278383320000000

#ed_atkins is exhibited at #stedelijk_museum

14281015920000000

#ed_atkins was exhibited at #isabella_bortolozzi_gallery

14281020510000000

#ed_atkins #ryan_trecartin

14281022700000000

#ryan_trecartin was exhibited at #kw_gallery

14281022910000000

#yuri_ancarani #harun_farocki

14281033480000000

#yuri_ancarani works were exhibited at #isabella_bortolozzi_gallery

14281033630000000

#harun_farocki is exhibited at #greene_naftali_gallery

14281033750000000

#yuri_ancarani da vinci https://www.youtube.com/watch?v=4cBoqgJfyXw and il capo https://www.youtube.com/watch?v=du9_Kn2y2VA

14281034600000000

#francis_alys #harun_farocki both deal with the fabric of the real

14281549430000000

#bjoern_dahlem at #berlinische_galerie

14281567970000000

#gianni_colombo #bjoern_dahlem structures

14281568240000000

#harun_farocki at #hkw_berlin

14281570270000000

#anthony_mccall and #james_turrell both work with light

14281570990000000

#anthony_mccall #hamburger_bahnhof

14281571080000000

#sebastião_salgado #co_berlin

14281685420000000

#richard_mosse #carlier_gebauer

14281685900000000

#paul_chan #jeremy_hutchison

14281693770000000

#giuseppe_penone #galerie_marian_goodman

14281700860000000

#wayne_mcgregor #michael_clark

14281701590000000

#richard_mosse #sebastião_salgado

14281701890000000

#tony_smith black geometrical shapes #james_turrell

14281705530000000

#tony_smith #giuseppe_penone

14281705860000000

#tony_smith is with #matthew_marks_gallery

14281706320000000

#thomas_demand #matthew_marks_gallery

14281707130000000

#yang_fudong #galerie_marian_goodman

14281713260000000

#yang_fudong #aes_group

14281714900000000

#aleksey_taruts #ed_atkins

14281715090000000

#aleksey_taruts #harun_farocki

14281715140000000

#katsuhiko_narita #tony_smith

14281715950000000

#ryan_mcginley #ryan_trecartin

14281716300000000

#ryan_mcginley #harmony_korine

14281717540000000

#harmony_korine #ryan_trecartin

14281717640000000

#bas_jan_ader #francis_alys

14281718890000000

#david_lachapelle #aes_group

14281719880000000

#erwin_olaf #david_lachapelle

14281722750000000

#jonathan_meese #oleg_kulik

14281722800000000

#gianni_colombo #tony_smith

14281723660000000

#almine_rech_gallery #erik_lindman

14281731320000000

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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

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):

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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
0	0	0	0

Discourse Advice:

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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)

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.