Upload README.md with huggingface_hub

README.md

@@ -1 +1,221 @@
-all of wikipedia mapped as a graph with edges and verticies
+---
+title: Wikipedia Link Graph and Layout Dataset (2026)
+emoji: 🌌
+colorFrom: indigo
+colorTo: purple
+sdk: static
+pretty_name: Wikipedia Link Graph & Layout Dataset
+dataset_info:
+  features:
+  - name: title
+    dtype: string
+  - name: idx
+    dtype: int64
+  - name: category
+    dtype: int64
+  - name: views
+    dtype: int64
+  - name: x
+    dtype: float64
+  - name: y
+    dtype: float64
+  splits:
+  - name: train
+    num_examples: 5483256
+tags:
+- graph
+- wikipedia
+- webgl
+- sqlite
+- rapids
+- network
+- link-prediction
+- community-detection
+- representation-learning
+size_categories:
+- 10M-100M
+---
+
+# Wikipedia Link Graph, layout, and Contexts Dataset
+
+This repository hosts the complete, high-fidelity graph dataset of the **English Wikipedia** (approx. **5.48M articles/nodes** and **100M+ links/edges**). It is designed to enable researchers, developers, and graph-database enthusiasts to study massive web graphs, run node classification, representation learning (Node2Vec, GNNs), and explore spatial force-directed graph layouts.
+
+This data directly backs the **Wikipedia Graph Visualizer**, an interactive cosmic WebGL space showing Wikipedia as a stellar galaxy.
+
+* **GitHub Repository:** [ICYBAWSS/wikipedia_graph](https://github.com/ICYBAWSS/wikipedia_graph)
+* **Interactive Visualizer:** [Live Demo](https://icybawss.github.io/wikipedia_graph/)
+
+---
+
+## 📁 File Structure & Specifications
+
+The dataset includes raw dumps, processed structured databases, optimized binary indices, and graph edge lists.
+
+| File Path in Repo | Size | Format | Description |
+| :--- | :--- | :--- | :--- |
+| `wiki_graph_structure.db` | **3.12 GB** | SQLite | Clean relational database of `nodes` and `links` tables. Ideal for general-purpose SQL queries. |
+| `test_scrape/wiki_simulation.db` | **25.30 GB** | SQLite | **Production Database**. Contains the node graph, full-text search indexes (`fts_idx`), and wikitext snippets surrounding links (`contexts` table) used by the visualizer. |
+| `test_scrape/wiki_graph.db` | **25.30 GB** | SQLite | Duplicate of `wiki_simulation.db` (retained for pipeline naming consistency). |
+| `test_scrape/wiki_cache.db` | **21.77 GB** | SQLite | Crawled and processed raw wikitext articles from the pipeline scraper. |
+| `test_scrape/enwiki-latest-pages-articles-multistream.xml.bz2` | **24.32 GB** | BZ2 | Raw XML Wikipedia multistream dump from Wikimedia. |
+| `test_scrape/pageviews.bz2` | **5.86 GB** | BZ2 | Raw monthly user pageview counts dump from Wikimedia. |
+| `edges_weighted.csv.gz` | **1.25 GB** | CSV (GZIP) | Tabular list of source and target node indices with weights. Useful for deep learning/GNN imports. |
+| `metadata.csv` | **138.97 MB** | CSV | Tabular index of node indices, titles, parent category IDs, and views metrics. |
+| `adjacency_csr.bin.gz` | **248.76 MB** | Binary (GZIP) | Packed Compressed Sparse Row (CSR) representation of out-edges for fast traversal. |
+| `adjacency_csr_rev.bin.gz` | **252.27 MB** | Binary (GZIP) | Packed Compressed Sparse Row (CSR) representation of in-edges (incoming links). |
+| `viewer_v2.bin.gz` | **35.18 MB** | Binary (GZIP) | Packed client-side array containing node indices, quantized coordinates, and node sizes. |
+| `titles_v2.bin.gz` | **47.63 MB** | Binary (GZIP) | Sequentially concatenated UTF-8 title byte index for zero-cost offset lookups. |
+
+---
+
+## 🏛️ Schema Definitions (SQLite)
+
+### 1. `wiki_graph_structure.db` (Clean Schema)
+
+This SQLite database contains the core relational schemas:
+
+* **`nodes` Table:**
+  ```sql
+  CREATE TABLE nodes (
+      idx INTEGER PRIMARY KEY,   -- 0-indexed node sequence ID
+      title TEXT UNIQUE,         -- Wikipedia Article Name (UTF-8)
+      category INTEGER,          -- Wikipedia Category ID mapping
+      views INTEGER,             -- Monthly Pageviews count
+      x INTEGER,                 -- Force-directed X coordinate (quantized uint16)
+      y INTEGER                  -- Force-directed Y coordinate (quantized uint16)
+  );
+  CREATE INDEX idx_nodes_title ON nodes(title);
+  ```
+
+* **`links` Table:**
+  ```sql
+  CREATE TABLE links (
+      source_idx INTEGER,        -- Source node idx
+      target_idx INTEGER,        -- Target node idx
+      FOREIGN KEY(source_idx) REFERENCES nodes(idx),
+      FOREIGN KEY(target_idx) REFERENCES nodes(idx)
+  );
+  CREATE INDEX idx_links_source ON links(source_idx);
+  CREATE INDEX idx_links_target ON links(target_idx);
+  ```
+
+### 2. `test_scrape/wiki_simulation.db` (Visualizer Backend Schema)
+
+This production database expands on the clean schema with full-text search indexes and link wikitext context snippets:
+
+* **`contexts` Table:**
+  ```sql
+  CREATE TABLE contexts (
+      source_idx INTEGER,        -- Source node idx
+      target_idx INTEGER,        -- Target node idx
+      context TEXT,              -- exact raw wikitext sentence containing the hyperlink
+      PRIMARY KEY (source_idx, target_idx)
+  );
+  ```
+
+---
+
+## ⚡ Loading & Access Examples
+
+### 1. SQLite Query Examples
+To find the shortest paths or navigate link hierarchies, query the SQLite database locally or stream it:
+
+```sql
+-- Get the out-links (pages mentioned in the article 'SpaceX')
+SELECT n.title 
+FROM links l 
+JOIN nodes n ON l.target_idx = n.idx 
+WHERE l.source_idx = (SELECT idx FROM nodes WHERE title = 'SpaceX');
+
+-- Get the in-links (pages linking back to 'Artificial intelligence')
+SELECT n.title 
+FROM links l 
+JOIN nodes n ON l.source_idx = n.idx 
+WHERE l.target_idx = (SELECT idx FROM nodes WHERE title = 'Artificial intelligence');
+
+-- Find context wikitext snippet explaining a connection
+SELECT context 
+FROM contexts 
+WHERE source_idx = (SELECT idx FROM nodes WHERE title = 'Python (programming language)')
+  AND target_idx = (SELECT idx FROM nodes WHERE title = 'C++');
+```
+
+### 2. Streaming via HTTP Range Requests (SQLite VFS)
+Because downloading the full `25.30 GB` database is impractical in the browser, the visualizer uses `sql-httpvfs` to stream chunks of the database directly from Hugging Face on-demand.
+
+#### Javascript/HTML integration:
+```javascript
+import { createDbWorker } from "sql-httpvfs";
+
+const workerUrl = new URL("sqlite.worker.js", import.meta.url).href;
+const wasmUrl = new URL("sql-wasm.wasm", import.meta.url).href;
+
+const dbUrl = "https://huggingface.co/datasets/icybawss/wikipedia-graph-data/resolve/main/test_scrape/wiki_simulation.db";
+
+const worker = await createDbWorker(
+  [
+    {
+      from: "inline",
+      config: {
+        serverMode: "full",
+        requestChunkSize: 65536, // 64KB range queries
+        url: dbUrl
+      }
+    }
+  ],
+  workerUrl,
+  wasmUrl
+);
+
+// Query is translated directly to HTTP 206 Partial Content range requests
+const results = await worker.db.query(
+  "SELECT context FROM contexts WHERE source_idx = 1010 AND target_idx = 2020"
+);
+console.log(results[0].context);
+```
+
+### 3. Reading CSR Traversal Binaries (Python)
+The Compressed Sparse Row binaries contain contiguous arrays of indices for instantaneous link graph traversals without SQL execution overhead.
+
+```python
+import numpy as np
+import gzip
+
+# Read packed CSR binary
+with gzip.open("adjacency_csr.bin.gz", "rb") as f:
+    # 32-bit integer header: [N, E]
+    header = np.frombuffer(f.read(8), dtype=np.uint32)
+    N, E = header[0], header[1]
+    
+    # Offsets array (size N + 1): points to starting bounds of target connections
+    offsets = np.frombuffer(f.read((N + 1) * 4), dtype=np.uint32)
+    
+    # Columns array (size E): stores the actual target indices
+    columns = np.frombuffer(f.read(E * 4), dtype=np.uint32)
+
+def get_neighbors(node_idx):
+    if node_idx < 0 or node_idx >= N:
+        return []
+    start = offsets[node_idx]
+    end = offsets[node_idx + 1]
+    return columns[start:end]
+
+print("Out-links for node ID 1010:", get_neighbors(1010))
+```
+
+---
+
+## ⚙️ Layout & Data Pipeline
+
+The dataset coordinates and binaries were generated via a multi-stage distributed pipeline:
+
+1. **Wikipedia Extraction:** Standard SAX parsing of `enwiki-latest-pages-articles-multistream.xml.bz2` extracting valid hyperlinks.
+2. **Pageviews Merging:** Joining nodes with monthly counts inside `pageviews.bz2` to compute node weight and relative radius sizing.
+3. **Layout Generation:** Running a **GPU-accelerated ForceAtlas2 force-directed physics layout** using **NVIDIA RAPIDS cuGraph** over the complete 100M+ link edge-list. 
+4. **Quantization:** Squeezing the double-precision float $(x, y)$ layout outputs into 16-bit unsigned integers mapped to a $384 \times 384$ coordinate grid system.
+5. **CSR Indexing:** Compiling out-links and in-links arrays to pack structural graph traversal bytes.
+
+---
+
+## ⚖️ Citation & License
+This dataset is compiled from the Wikimedia XML database dumps and pageviews files, which are distributed under the **Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)**. All code and scripts in the accompanying GitHub repository are licensed under the **MIT License**.