lukeslp/steam-co-review-network

Steam Co-Review Network

Two Steam games share an edge when multiple users reviewed both. Built from 128 million user reviews across 80,000 games (2012-2024), this dataset maps how the Steam catalog is connected through player overlap.

Files

steam_network_full.json

The complete co-review graph with minimal filtering:

• 48,362 game nodes (every game with 10+ reviews)
• 33,041,298 weighted edges (2+ shared reviewers per pair)
• Per-node cap of 50 neighbors (densest connections preserved)
• Edge weights range from 2 to 420,410 shared reviewers

Node format:

{
  "id": "620",
  "title": "Portal 2",
  "year": "2011",
  "rating": "Overwhelmingly Positive",
  "ratio": 97,
  "reviews": 263842,
  "price": 9.99
}

Link format:

{"source": 0, "target": 42, "weight": 1523}

Source and target are indices into the nodes array. Weight is the number of users who reviewed both games.

steam_all_2005.json

82,928 games released 2005-2025. Packed as arrays for compact JSON:

[name, year, ratio, reviews, price, ratingIdx, genreIdxs, tagIdxs, developer]
 [0]    [1]   [2]    [3]      [4]    [5]        [6]        [7]       [8]

Genres and tags are stored as index arrays referencing top-level genres[] and tags[] lookup tables in the same file.

steam_force_layout.json

Genre-aware pre-computed layout positions for the top ~9K nodes, clustered by primary genre with hub games as anchors. Use as warm-start coordinates for force-directed visualization.

Sources

• Game metadata: FronkonGames Steam Games Dataset — Jan 2026 snapshot, 122K games
• User reviews: artermiloff Steam Reviews 2024 — 128M reviews across 80K games, one CSV per game, 2012-June 2024

Pipeline

1. Load game metadata from FronkonGames enriched CSV
2. Scan 30K+ per-game review CSVs, extract steamid-to-game mappings
3. For each user who reviewed 2+ games, generate all game pairs
4. Count shared reviewers per pair to produce edge weights
5. Filter: minimum 2 shared reviewers (no neighbor cap)

Full pipeline: github.com/lukeslp/steam-network-data

Use Cases

• Graph ML: Node classification (predict genre/rating from network position), link prediction, community detection
• Recommendation systems: Games connected by high edge weights share audiences
• Market analysis: Which genres cluster together? Where are the gaps?
• Visualization: Force-directed layouts, chord diagrams, genre timelines of the Steam ecosystem

Live Visualization

dr.eamer.dev/datavis/interactive/steam/

Four interactive Canvas-rendered views: universe scatter, chord diagram, force-directed network, and genre timeline.

Distribution

• GitHub: lukeslp/steam-network-data
• Kaggle: lucassteuber/steam-universe-network

Author

Luke Steuber — lukesteuber.com — @lukesteuber.com on Bluesky