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Diffusion Source Identification on Networks with Statistical Confidence

Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.

This is an updated version of the code. Previous version can be found in the legacy directory.

Installation

The code can be installed by running the following commands

 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.

diffusion_source.graphs

Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.

diffusion_source.infection_model

A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.

diffusion_source.discrepancies

A number of discrepancy functions are written for use when running the confidence set algorithm

diffusion_source.display

A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.

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Source code for diffusion source identification project

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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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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
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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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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
Resources
Readme
Activity
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3 stars
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1 watching
Forks
1 fork
Report repository
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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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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
1 watching
Forks
1 fork
Report repository
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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" + '

Skip to content
BranchesTags
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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
1 watching
Forks
1 fork
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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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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
Resources
Readme
Activity
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Stars
3 stars
Watchers
1 watching
Forks
1 fork
Report repository
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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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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
1 watching
Forks
1 fork
Report repository
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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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Diffusion Source Identification on Networks with Statistical Confidence
Source code for confidence set analysis on the Diffusion Source Identification (DSI) problem. Paper presented at ICML 2021 can be found at ICML 2021's Paper List or on arXiv.
This is an updated version of the code. Previous version can be found in the legacy directory.
Installation
The code can be installed by running the following commands
 git clone https://github.com/lab-sigma/Diffusion-Source-Identification
cd Diffusion-Source-Identification
pip install .

The package is named ``diffusion_source''. The relevant subpackages are summarized below.
diffusion_source.graphs
Provides a wrapper around a networkx graph used by the infection model. A superset of the synthetic networks used in the paper are provided, as well as classes for importing a graph from adjacency formats and an already created networkx graph.
diffusion_source.infection_model
A base abstract infection model class is provided as ``InfectionModelBase'' that includes a definition of the confidence set function for arbitrary diffusion processes.
diffusion_source.discrepancies
A number of discrepancy functions are written for use when running the confidence set algorithm
diffusion_source.display
A set of tools for displaying example infected sets and confidence sets, as well as generating summary figures for large scale model tests.
About
Source code for diffusion source identification project
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
1 watching
Forks
1 fork
Report repository
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