Datasets:
File size: 4,518 Bytes
d60bb92 70e2fe1 d60bb92 70e2fe1 dc70da8 df3e6b7 2fe4312 70e2fe1 2fe4312 df3e6b7 70e2fe1 4c9066d df3e6b7 70e2fe1 c3d16c9 df3e6b7 70e2fe1 4bedb5e df3e6b7 70e2fe1 d60bb92 df3e6b7 eebf162 70e2fe1 df3e6b7 70e2fe1 eebf162 df3e6b7 3d69343 70e2fe1 3d69343 df3e6b7 70e2fe1 458e54a df3e6b7 70e2fe1 01e1209 df3e6b7 d60bb92 70e2fe1 eb253f6 70e2fe1 df3e6b7 b942fac df3e6b7 70e2fe1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | ---
pretty_name: Paper Universe Graph
viewer: true
tags:
- datasets
- graph
- scientific-papers
- arxiv
- retrieval
- embeddings
size_categories:
- 10M<n<100M
configs:
- config_name: paper_nodes
default: true
data_files:
- split: train
path: "paper_nodes/*.parquet"
- config_name: paper_category_edges
default: false
data_files:
- split: train
path: "paper_category_edges/*.parquet"
- config_name: paper_knn
default: false
data_files:
- split: train
path: "paper_knn/*.parquet"
- config_name: category_nodes
default: false
data_files:
- split: train
path: "category_nodes/*.parquet"
- config_name: category_knn
default: false
data_files:
- split: train
path: "category_knn/*.parquet"
- config_name: topic_nodes
default: false
data_files:
- split: train
path: "topic_nodes/*.parquet"
- config_name: paper_topic_edges
default: false
data_files:
- split: train
path: "paper_topic_edges/*.parquet"
- config_name: year_nodes
default: false
data_files:
- split: train
path: "year_nodes/*.parquet"
- config_name: paper_year_edges
default: false
data_files:
- split: train
path: "paper_year_edges/*.parquet"
- config_name: paper_embeddings
default: false
data_files:
- split: train
path: "paper_embeddings/*.parquet"
- config_name: paper_fulltext_embeddings
default: false
data_files:
- split: train
path: "paper_fulltext_embeddings/*.parquet"
---
# Generated with Research Library:
https://github.com/peytontolbert/Research_Library
# Paper Universe Graph Dataset
Parquet-first export of the paper-universe graph already built under the Repository Library.
This dataset preserves:
- paper nodes with metadata references and 3D coordinates
- paper/category/year/topic graph layers
- optional paper-to-paper and category-to-category similarity edges
- metadata and full-text paper embedding splits
## Coverage
- papers: `1000000`
- categories: `156`
- years: `19`
- topics: `1483957`
- embedding dimension: `384`
- full-text embeddings included: `true`
## Configs
- default viewer/config: `paper_nodes`
- `paper_nodes`: `1000000` rows
- `paper_category_edges`: `1744492` rows
- `paper_knn`: `20000062` rows
- `category_nodes`: `156` rows
- `category_knn`: `1248` rows
- `topic_nodes`: `1483957` rows
- `paper_topic_edges`: `3000000` rows
- `year_nodes`: `19` rows
- `paper_year_edges`: `1000000` rows
- `paper_embeddings`: `1000000` rows
- `paper_fulltext_embeddings`: `1000000` rows
## Loading
```python
from datasets import load_dataset
paper_nodes = load_dataset("PeytonT/paper_graph", "paper_nodes")
paper_category_edges = load_dataset("PeytonT/paper_graph", "paper_category_edges")
paper_knn = load_dataset("PeytonT/paper_graph", "paper_knn")
category_nodes = load_dataset("PeytonT/paper_graph", "category_nodes")
category_knn = load_dataset("PeytonT/paper_graph", "category_knn")
topic_nodes = load_dataset("PeytonT/paper_graph", "topic_nodes")
paper_topic_edges = load_dataset("PeytonT/paper_graph", "paper_topic_edges")
year_nodes = load_dataset("PeytonT/paper_graph", "year_nodes")
paper_year_edges = load_dataset("PeytonT/paper_graph", "paper_year_edges")
paper_embeddings = load_dataset("PeytonT/paper_graph", "paper_embeddings")
paper_fulltext_embeddings = load_dataset("PeytonT/paper_graph", "paper_fulltext_embeddings")
```
## Notes
- `paper_nodes` stores metadata references and coordinates, not the full paper body.
- The original full text remains in the source paper dataset referenced by the manifest.
- `paper_embeddings` is the metadata/title+abstract embedding split.
- `paper_fulltext_embeddings` is the aggregated full-body embedding split when available.
## Visualization Assets
The export includes the local paper-universe visualizations and viewer payload when present:
- `manifest.json`
- `progress.json`
- `render_manifest.json`
- `viewer_manifest.json`
- `universe_3d.png`
- `universe_3d_detailed.png`
- `nodes_3d_sample.html`
- `universe_3d_hover.html`
- `interactive/categories.json`
- `interactive/manifest.json`
- `interactive/papers_200000.json`
- `interactive/papers_50000.json`
- `interactive/years.json`
### Universe 3D Overview

### Universe 3D Detailed View

- [Open the sampled 3D HTML view](./nodes_3d_sample.html)
- [Open the interactive hover view](./universe_3d_hover.html)
- Interactive viewer payload is included under `interactive/`.
|