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keygraph

The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.

Installation

Clone the repository, then run setup.py to install the required NLTK resources:

python3 setup.py

To run the scripts you need to have Python installed.

Usage

Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:

python3 keygraph.py d1

This creates the file d1.html in the graphs folder. Open this file to view the keygraph.

A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.

When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.

There are two hyper-parameters that affect the content of the keygraph:

  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters

Both are used to eliminate words and connections from the keygraph:

  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances

Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).

Web-based version

A web-based version to access the tool is under development.

References

Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.

Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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keygraph
The goal of this project is to implement the KeyGraph algorithm for chance discovery (Ohsawa et al., 1998; Ohsawa, 2006). It builds on this project: https://github.com/ShinsakuSegawa/keygraph.
Installation
Clone the repository, then run setup.py to install the required NLTK resources:
python3 setup.py
To run the scripts you need to have Python installed.
Usage
Suppose the document you want to analyze is in the file d1.txt in the txt_files folder. To create a keygraph from the text in this document run:
python3 keygraph.py d1
This creates the file d1.html in the graphs folder. Open this file to view the keygraph.
A keygraph consists of clusters of black nodes, and red nodes. Clusters of black nodes represent established concepts. Red nodes represent chances which can be interpreted as new concepts. The output of the keygraph is a set of scenarios formed by combining chances with the clusters they connect.
When generating a keygraph, stopwords in the noise\stopwords.txt file are used to remove noise words. To add more stopwords, add one stopword per line.
There are two hyper-parameters that affect the content of the keygraph:
  • $M$ is the number of high frequency words, as well as the maximum number of connections between them
  • $K$ is the number of keys (chances) and the maximum number of links between keys and clusters
Both are used to eliminate words and connections from the keygraph:
  • $M$ is used during the selection of black nodes and the creation of clusters of black nodes
  • $K$ is the number of red nodes which connect or bridge clusters and represent chances
Note that both are upper limits: nodes will only be shown if they are connected to other nodes after the two selection steps (selection of high frequency words and identification of chances).
Web-based version
A web-based version to access the tool is under development.
References
Ohsawa, Y., Benson, N. E., & Yachida, M. (1998). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. International Forum on Research and Technology Advances in Digital Libraries (ADL), 12-18). IEEE.
Ohsawa, Y. (2006). Chance discovery: The current states of art. In: Ohsawa, Y., & Tsumoko, E. (eds.), Chance Discoveries in Real World Decision Making, 3-20. Springer.
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