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Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy

Repository files navigation

This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation

Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.

Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.

Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)

Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.

Connect with me:

GitHub icon | LinkedIn icon | Twitter icon

Explore more articles on Medium and follow my GitHub for AI projects.

About

This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn

Topics

deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization

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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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Step 0_accesing azure datalake.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
Repository files navigation
This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
GitHub icon | LinkedIn icon | Twitter icon
Explore more articles on Medium and follow my GitHub for AI projects.
About
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
Resources
Readme
Activity
Stars
2 stars
Watchers
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1 fork
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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); }
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(function(){
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Step 0_accesing azure datalake.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
Repository files navigation
This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
GitHub icon | LinkedIn icon | Twitter icon
Explore more articles on Medium and follow my GitHub for AI projects.
About
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
Resources
Readme
Activity
Stars
2 stars
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2 watching
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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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Step 0_accesing azure datalake.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
Repository files navigation
This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
GitHub icon | LinkedIn icon | Twitter icon
Explore more articles on Medium and follow my GitHub for AI projects.
About
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
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1 fork
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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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Step 0_accesing azure datalake.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
Repository files navigation
This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
GitHub icon | LinkedIn icon | Twitter icon
Explore more articles on Medium and follow my GitHub for AI projects.
About
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
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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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Step 0_accesing azure datalake.ipynb
Step 0_accesing azure datalake.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
Repository files navigation
This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
GitHub icon | LinkedIn icon | Twitter icon
Explore more articles on Medium and follow my GitHub for AI projects.
About
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
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1 fork
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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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Step 0_accesing azure datalake.ipynb
Step 0_accesing azure datalake.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
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This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
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Explore more articles on Medium and follow my GitHub for AI projects.
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This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
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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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Step 0_accesing azure datalake.ipynb
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Step 1_graph_construction_and_metrics.ipynb
Step 1_graph_construction_and_metrics.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 2_knowledge_graphml_node2vec.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 3_gnn_geometric_pytorch.ipynb
Step 4_clustering_using_node_embeddings.ipynb
Step 4_clustering_using_node_embeddings.ipynb
combined_data.parquet
combined_data.parquet
gcn_model.pth
gcn_model.pth
gnn_embeddings.npy
gnn_embeddings.npy
gnn_embeddings.tsv
gnn_embeddings.tsv
graph.graphml
graph.graphml
kmeans_labels.npy
kmeans_labels.npy
metadata.tsv
metadata.tsv
node2vec_embeddings.kv
node2vec_embeddings.kv
node_ids.npy
node_ids.npy
Repository files navigation
This repository shows a use case of Graph Machine Learning for Casino Industry Marketing: Customer Segmentation
Step 0_accesing azure datalake: notebok to azure datalake and create the parquet file that merges all blobs. It uses Azure SDK to access the blobs.
Step 1_graph_construction_and_metrics: notebook that create the Graph (nodes and edges) and explore the metrics : degree centrality, Betweenness centrality, Closeness centrality, EigenCentrality tod escribe the topology of the graph. Basically, NetworkX python package is used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Step 2_knowledge_graphml_node2vec: it uses node2vec to get node embeddings. These nodes embeddings represent only the topology of the network.
Step 3_gnn_geometric_pytorch: GNN with geometric pytorch using node embeddings from previous notebook plus node features and egde features (edge weights)
Step 4_clustering_using_node_embeddings: using KMeans, it uses the node embeddings from geometric pytorch to cluser them and get the expected market segmentation.
Connect with me:
GitHub icon | LinkedIn icon | Twitter icon
Explore more articles on Medium and follow my GitHub for AI projects.
About
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
Topics
deep-learninggnn-learninggraphmlkmeans-clusteringnode2vec-embeddingspytorchregularization
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages