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Graph Encoder Embedding

This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.

The Main folder contains the core GraphEncoder function in three languages:

  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R

And several extension functions for MATLAB (not yet ported to Python or R):

  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)

The Data folder contains the public real data used in the reference papers.

The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.

Basic Usage in MATLAB:

Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding

Z=GraphEncoder(A,Y); where Z is the n*K vertex embedding.

Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:

[Z,Dynamic]=TemporalGraph(E,Y); where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.

Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:

[Z,Y]=UnsupGraph(A,K); where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.

References:

  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098

  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290

  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.

  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051

  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797

  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __m = "github.com";
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Repository files navigation
Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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Repository files navigation
Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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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");
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Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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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");
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Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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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");
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Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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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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Repository files navigation
Graph Encoder Embedding
This github repo provides a working code for graph encoder embedding, which is updated regularly to reflect our research progress.
The Main folder contains the core GraphEncoder function in three languages:
  • MATLAB: GraphEncoder.m
  • Python: GraphEncoder.ipynb
  • R: GraphEncoder.R
And several extension functions for MATLAB (not yet ported to Python or R):
  • GraphCorr.m (Graph correlation between multiple graphs with same vertex label)
  • TemporalGraph.m (Temporal GEE for multiple graphs with same vertex label)
  • UnsupGraph.m (Unsupervised GEE for graph without vertex label)
  • RefinedGEE.m (Refined GEE for improved classification)
The Data folder contains the public real data used in the reference papers.
The Experiments folder contains various experiments, plots, and auxiliary functions for the reference papers.
Basic Usage in MATLAB:
Given a graph A (either an nn square matrix or an s3 edgelist) and corresponding label vector Y (n*1 vector with K classes), the following outputs the supervised graph encoder embedding
Z=GraphEncoder(A,Y);
where Z is the n*K vertex embedding.
Given a time-series graph A (stored in a 1*T cell, and each cell can be either square matrix of edgelist), and a label vector Y, the following outputs the temporal embedding:
[Z,Dynamic]=TemporalGraph(E,Y);
where Dynamic contains the vertex, community, and graph dynamic in a 1*3 cell output.
Given a graph A and desired number of class K (or a range), the following outputs the unsupervised embedding:
[Z,Y]=UnsupGraph(A,K);
where Z is the unsupervised vertex embedding, and Y is the estimated class label vector for each vertex.
References:
  1. C. Shen and Q. Wang and C. E. Priebe, "One-Hot Graph Encoder Embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7933 - 7938, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3225073, arXiv:2109.13098
  2. C. Shen and Y. Park and C. E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, pp. 13-18, ACM, 2023. DOI: https://doi.org/10.1145/3625403.3625407, arXiv:2301.11290
  3. C. Shen, J. Larson, H. Trinh, X. Qin, Y. Park, and C. E. Priebe, "Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics", IEEE Transactions on Network Science and Engineering, 11(2):2100 - 2109, 2024. DOI: https://doi.org/10.1109/TNSE.2023.3337600, arXiv:2305.02381.
  4. C. Shen, C. E. Priebe, J. Larson, H. Trinh, "Synergistic Graph Fusion via Encoder Embedding", Information Sciences, 120912, 2024. DOI: https://doi.org/10.1016/j.ins.2024.120912. arXiv:2303.18051
  5. C. Shen, J. Larson, H. Trinh, and C. E. Priebe, "Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery". arXiv:2405.12797
  6. C. Shen, J. Arroyo, J. Xiong, and J. T. Vogelstein, "Graph Independence Testing via Encoder Embedding and Community Correlations". arXiv:1906.03661
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