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

15647790591630000

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PNG (Image) SVG (Hi-Res)

JSON CSV Gexf (Gephi)

CSV tagged w Topics Blocks with Topics Plain Text

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Download the Graph Data:

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Download the Text Data:

CSV tagged w Topics Blocks with Topics Plain Text

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

15647790591630000

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

15647797193210000

#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

15647801185840000

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

15647802901380000

@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

15647806553620000

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

15647807139410000

#kumu basically creates #mind_maps

15647807815740000

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

15647822025120000

#infranodus works on #neo4j

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

15647822652680000

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

15647823416760000

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

15647830919630000

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

15647834622290000

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

15647839789010000

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

15647851244220000

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

15648297725760000

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

15648299385550000

#kumu can be used for #storytelling

15648299499050000

@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)

15648348224610000

@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/

15648350141910000

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

15648350344690000

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

15771208755330000

#graph_commons is best suited for #storytelling using the graphs

15771208973330000

@cytoscape performs #network_analysis and #network_visualization

15771245352850000

#infranodus is a #network_visualization tool

15771245462180000

#arcade_analytics can visualise #neo4j data

15771245655590000

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

15771246373970000

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

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#infranodus can visualise a network of #concepts

15771250183270000

#infranodus can be used to create #mind_maps

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

15771251933340000

@nodexl is an #excel tool for #network_analysis

15771257953670000

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

15771290976340000

@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.

16066749278030000

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

16066750118830000

@sigma_js is a #javascript library for #network_visualization

16066750379130000

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

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A lot of #linkurious was written on #sigma_js

16066750574430000

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

16066750748440000

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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.

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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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.

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.

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.

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) ? 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.

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.

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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positive: | negative: | neutral:

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Main Topical Groups:

N/A

+ ⤓ ? ⤓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 and are given a distinct color.

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 and are given a distinct color.

Most Influential Elements:

N/A

+ ⤓ ↻ ?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.

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.

Reset Graph Export: Show Options

Action Advice:

N/A

Structural Gap

(ask a research question that would link these two topics):N/A

?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.

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

:
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.

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

(frequency / time) ?
↻

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 frequency of occurrence.

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).

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):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.

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):Sentiment Analysis

positive: | negative: | neutral:

⤓ ? ⤓

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

The approach is based on AFINN and Emoji Sentiment Ranking

Network Statistics:

Top Relations / Bigrams

(both directions):
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).

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).

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