people who search for #y_combinator also search for #Techstars
14269484340000000
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InfraNodus
Project Notes:
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.
people who search for #y_combinator also search for #Andreessen_Horowitz
14269484340000000
people who search for #y_combinator also search for #Google_Ventures
14269484340000000
people who search for #y_combinator also search for #AngelPad
14269484340000000
people who search for #y_combinator also search for #Sequoia_Capital
14269484340000000
people who search for #y_combinator also search for #Loopt
14269484340000000
people who search for #y_combinator also search for #Quora
14269484340000000
people who search for #Techstars also search for #Y_Combinator
14269484460000000
people who search for #Techstars also search for #Seedcamp
14269484460000000
people who search for #Techstars also search for #AngelPad
14269484460000000
people who search for #Techstars also search for #SendGrid
14269484460000000
people who search for #Techstars also search for #Google_Ventures
14269484460000000
people who search for #Techstars also search for #Andreessen_Horowitz
14269484460000000
people who search for #Techstars also search for #Next_Big_Sound
14269484460000000
people who search for #Angelpad also search for #Techstars
14269484610000000
people who search for #Angelpad also search for #Y_Combinator
14269484610000000
people who search for #Angelpad also search for #Crittercism
14269484610000000
people who search for #Angelpad also search for #Google_Ventures
14269484610000000
people who search for #Angelpad also search for #Storefront
14269484610000000
people who search for #Angelpad also search for #Seedcamp
14269484610000000
people who search for #Angelpad also search for #Andreessen_Horowitz
14269484610000000
people who search for #Google_Ventures also search for #Andreessen_Horowitz
14269484740000000
people who search for #Google_Ventures also search for #Kleiner_Perkins_Caufield_and_Byers
14269484740000000
people who search for #Google_Ventures also search for #Uber
14269484740000000
people who search for #Google_Ventures also search for #New_Enterprise_Associates
14269484740000000
people who search for #Google_Ventures also search for #Y_Combinator
14269484740000000
people who search for #Google_Ventures also search for #Sidecar
14269484740000000
people who search for #Google_Ventures also search for #First_Round_Capital
14269484740000000
people who search for #Andreessen_Horowitz also search for #Google_Ventures
14269484880000000
people who search for #Andreessen_Horowitz also search for #Kleiner_Perkins_Caufield_and_Byers
14269484880000000
people who search for #Andreessen_Horowitz also search for #New_Enterprise_Associates
14269484880000000
people who search for #Andreessen_Horowitz also search for #Sequoia_Capital
14269484880000000
people who search for #Andreessen_Horowitz also search for #Greylock_Partners
14269484880000000
people who search for #Andreessen_Horowitz also search for #Y_Combinator
14269484880000000
people who search for #Andreessen_Horowitz also search for #Accel_Partners
14269484880000000
people who search for #Sequoia_Capital also search for #Kleiner_Perkins_Caufield_and_Byers
14269485040000000
people who search for #Sequoia_Capital also search for #Accel_Partners
14269485040000000
people who search for #Sequoia_Capital also search for #Andreessen_Horowitz
14269485040000000
people who search for #Sequoia_Capital also search for #Google
14269485040000000
people who search for #Sequoia_Capital also search for #Greylock_Partners
14269485040000000
people who search for #Sequoia_Capital also search for #Khosla_Ventures
14269485040000000
people who search for #Sequoia_Capital also search for #New_Enterprise_Associates
14269485040000000
people who search for #New_Enterprise_Associates also search for #Kleiner_Perkins_Caufield_and_Byers
14269485260000000
people who search for #New_Enterprise_Associates also search for #Andreessen_Horowitz
14269485260000000
people who search for #New_Enterprise_Associates also search for #Google_Ventures
14269485260000000
people who search for #New_Enterprise_Associates also search for #Accel_Partners
14269485260000000
people who search for #New_Enterprise_Associates also search for #Sequoia_Capital
14269485260000000
people who search for #New_Enterprise_Associates also search for #Lightspeed_Venture_Partners
14269485260000000
people who search for #New_Enterprise_Associates also search for #Draper_Fisher_Jurvetson
14269485260000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #Sequoia_Capital
14269485410000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #New_Enterprise_Associates
14269485410000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #Andreessen_Horowitz
14269485410000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #Google_Ventures
14269485410000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #Accel_Partners
14269485410000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #Khosla_Ventures
14269485410000000
people who search for #Kleiner_Perkins_Caufield_And_Byers also search for #Lightspeed_Venture_Partners
14269485410000000
people who search for #Uber also search for #Sidecar
14269485620000000
people who search for #Uber also search for #Google_Ventures
14269485620000000
people who search for #Uber also search for #First_Round_Capital
14269485620000000
people who search for #Loopt also search for #Y_Combinator
14269486550000000
people who search for #Loopt also search for #SCVNGR
14269486550000000
people who search for #Loopt also search for #Green_Dot_Corporation
14269486550000000
#airbnb was funded by #y_combinator
14269487640000000
people who search for #Seedcamp also search for #Techstars
14269487820000000
people who search for #Seedcamp also search for #Index_Ventures
14269487820000000
people who search for #Seedcamp also search for #Y_Combinator
14269487820000000
people who search for #Seedcamp also search for #AngelPad
14269487820000000
people who search for #Seedcamp also search for #Atlas_Venture
14269487820000000
people who search for #Seedcamp also search for #LoveFilm
14269487820000000
people who search for #Seedcamp also search for #Accel_Partners
14269487820000000
people who search for #Accel_Partners also search for #Sequoia_Capital
14269488430000000
people who search for #Accel_Partners also search for #Accel-KKR
14269488430000000
people who search for #Accel_Partners also search for #Greylock_Partners
14269488430000000
people who search for #Accel_Partners also search for #Kleiner_Perkins_Caufield_and_Byers
14269488430000000
people who search for #Accel_Partners also search for #Andreessen_Horowitz
14269488430000000
people who search for #Accel_Partners also search for #Index_Ventures
14269488430000000
people who search for #Accel_Partners also search for #Lightspeed_Venture_Partners
14269488430000000
people who search for #Khosla_Ventures also search for #Kleiner_Perkins_Caufield_and_Byers
14269489040000000
people who search for #Khosla_Ventures also search for #Sequoia_Capital
14269489040000000
people who search for #Khosla_Ventures also search for #Andreessen_Horowitz
14269489040000000
people who search for #Khosla_Ventures also search for #Battery_Ventures_LP
14269489040000000
people who search for #Khosla_Ventures also search for #EcoMotors
14269489040000000
people who search for #Khosla_Ventures also search for #Founders_Fund
14269489040000000
people who search for #Khosla_Ventures also search for #Greylock_Partners
14269489040000000
people who search for #Greylock_Partners also search for #Accel_Partners
14269489180000000
people who search for #Greylock_Partners also search for #Sequoia_Capital
14269489180000000
people who search for #Greylock_Partners also search for #Andreessen_Horowitz
14269489180000000
people who search for #Greylock_Partners also search for #Redpoint
14269489180000000
people who search for #Greylock_Partners also search for #Benchmark
14269489180000000
people who search for #Greylock_Partners also search for #Kleiner_Perkins_Caufield_and_Byers
14269489180000000
people who search for #Greylock_Partners also search for #Founders_Fund
14269489180000000
people who search for #Atlas_Venture also search for #Polaris_Partners
14269489430000000
people who search for #Atlas_Venture also search for #Highland_Capital_Partners
14269489430000000
people who search for #Atlas_Venture also search for #Charles_River_Ventures
14269489430000000
people who search for #Atlas_Venture also search for #Bessemer_Venture_Partners
14269489430000000
people who search for #Atlas_Venture also search for #Flybridge_Capital_Partners
14269489430000000
people who search for #Atlas_Venture also search for #Accel_Partners
14269489430000000
people who search for #Atlas_Venture also search for #General_Catalyst_Partners
14269489430000000
people who search for #Index_Ventures also search for #Accel_Partners
14269489930000000
people who search for #Index_Ventures also search for #Greylock_Partners
14269489930000000
people who search for #Index_Ventures also search for #Redpoint
14269489930000000
people who search for #Index_Ventures also search for #Seedcamp
14269489930000000
people who search for #Index_Ventures also search for #Benchmark
14269489930000000
people who search for #Index_Ventures also search for #Insight_Venture_Partners
14269489930000000
people who search for #Index_Ventures also search for #Highland_Capital_Partners
14269489930000000
people who search for #Highland_Capital_Partners also search for #Highland_Capital_Management
14269503010000000
people who search for #Highland_Capital_Partners also search for #Atlas_Venture
14269503010000000
people who search for #Highland_Capital_Partners also search for #General_Catalyst_Partners
14269503010000000
people who search for #Highland_Capital_Partners also search for #Bessemer_Venture_Partners
14269503010000000
people who search for #Highland_Capital_Partners also search for #Index_Ventures
14269503010000000
people who search for #Highland_Capital_Partners also search for #Flybridge_Capital_Partners
14269503010000000
people who search for #Highland_Capital_Partners also search for #Charles_River_Ventures
14269503010000000
people who search for #Founders_Fund also search for #Clarium_Capital
14269503320000000
people who search for #Founders_Fund also search for #Palantir_Technologies
14269503320000000
people who search for #Founders_Fund also search for #Greylock_Partners
14269503320000000
people who search for #Founders_Fund also search for #Andreessen_Horowitz
14269503320000000
people who search for #Founders_Fund also search for #Khosla_Ventures
14269503320000000
people who search for #Founders_Fund also search for #Draper_Fisher_Jurvetson
14269503320000000
people who search for #Founders_Fund also search for #Accel_Partners
14269503320000000
people who search for #First_Round_Capital also search for #Accel_Partners
14269503930000000
people who search for #First_Round_Capital also search for #Andreessen_Horowitz
14269503930000000
people who search for #First_Round_Capital also search for #General_Catalyst_Partners
14269503930000000
people who search for #First_Round_Capital also search for #Google_Ventures
14269503930000000
people who search for #First_Round_Capital also search for #Draper_Fisher_Jurvetson
14269503930000000
people who search for #First_Round_Capital also search for #New_Enterprise_Associates
14269503930000000
people who search for #First_Round_Capital also search for #Venrock
14269503930000000
#quid was financed by #founders_fund in round C in 2011
14269507990000000
#horizon_ventures invested in #summly at the early stage
14269509360000000
#horizon_ventures invested in #facebook in 2007 and 2008
14269510240000000
#horizon_ventures invested in #spotify in 2008
14269510490000000
#atomico invested in #visualdna
14269510920000000
#horizon_ventures invested in #visualdna
14269511020000000
#atomico invested in #quid
14269511860000000
#atomico co-invested with #index_ventures
14269512530000000
#atomico coinvested with #accel_partners and #founders_fund
14269512680000000
#horizon_ventures invested in #summly
14269513650000000
#betaworks invested in #summly
14269514660000000
people who search for #Atomico also search for #Accel_Partners
14269515040000000
people who search for #Atomico also search for #Wellington_Partners
14269515040000000
people who search for #Atomico also search for #Index_Ventures
14269515040000000
people who search for #Atomico also search for #Marcandi_Ltd
14269515040000000
people who search for #Atomico also search for #The_Climate_Corporation
14269515040000000
people who search for #Atomico also search for #Knewton
14269515040000000
people who search for #Atomico also search for #Greylock_Partners
14269515040000000
#betaworks and #founders_fund coinvested in several companies together
14269516380000000
#betaworks is interested in doing seed funding and in attracting the founders who don't want to necessarily run only one company https://www.crunchbase.com/organization/betaworks
14269517440000000
#betaworks is more into applied tools such as #bitly and #tweetdeck
14269518480000000
#dawn_capital invested in #neo_technology - the maker of neo4j graph database
14269520590000000
#dawn_capital coinvested with #index_ventures
14269520810000000
#dawn_capital coinvested with #greylock_partners
14269520980000000
#dawn_capital seems to be interested in infrastructure
14269521650000000
#yuuwa_capital invested in #discovr
14269527980000000
#yuuwa_capital is interested in biotech mainly
14269528510000000
#sparkbox_ventures invested in #mohiomaps and #snakk_media
14269529860000000
#sparkbox_ventures is a bit like #yuuwa_capital in its preference to biotech companies. the first one is in NZ and the second one is in Australia
14269530190000000
#seedcamp invested in #uberVU
14269535030000000
#seedcamp invested in #wordy
14269535910000000
#seedcamp invested in #mytip
14269537300000000
#seedcamp seems to be interested in applications for web
14269537470000000
#khosla_ventures investors invested in #palantir_technologies
14269541940000000
#techstars invests into financial and curated web startups
14269543390000000
#y_combinator invested in #yhat
14269556510000000
#y_combinator invested in #meta augmented reality hardware and software startup
14269558290000000
#y_combinator invested in #booktrope
14269559230000000
#andreessen_horowitz invested in #instagram
14269583270000000
#Liberty_Interactive_Corporation invested into #quid
15873937580000000
[] [quid]
#artis_ventures, #buchanan_investments, #subtraction_capital, #tiger_partners, #thomas_lee_limited_family @quid
15873938300000000
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Network Diversity Phase:
how it works? 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.
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.
× ⁝⁝
Reset Graph Export: Show Options
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.
positive: | negative: | neutral:
⤓ ? ⤓
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.
?
Main Topical Groups:
please, add your data to display the stats...
+ ⤓ ? ⤓ 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.
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...
+ ⤓ ↻ ?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.
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
? 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
(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.
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).
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.
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:
⤓ ? ⤓
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
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...
+ ?
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. 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).
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:
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.
| 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 |
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.