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Tessera DB Tutorial: Run PageRank over Your Graph in 5 Minutes
Score every entity by its centrality in the graph. Useful for fraud rings, influence networks, and feature engineering for ML.
PAR2 Labs
August 29, 2026
3 min
PageRank is the canonical centrality algorithm. It tells you which nodes are "important" in the structural sense — recursively, a node is important if other important nodes point to it.
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Before you start
Tier
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Graph Data Science — the 60+ graph algorithms, PageRank among them — is an Enterprise feature.
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Step 1: Load some graph data
Either load the sample social network from the admin dashboard, or ingest your own data with at least one relationship type that has two endpoints — that's the minimum for a graph to walk.
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Step 2: Build a projection
Graph algorithms run against a projection — a named, in-memory snapshot of your data shaped for analysis, optionally filtered by type and treated as directed or undirected. Build it once and run many analyses against it.
Build it once and run many analyses against it.
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Step 3: Run PageRank
Run PageRank over the projection with the usual controls — a damping factor (0.85 is the conventional default) and an iteration cap. Each node comes back with a score; sort descending to see the most central entities. You can drive this from the admin dashboard's graph tools or through the query layer, whichever fits your workflow.
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Common uses
Fraud detection — high-PageRank accounts in a transaction graph are often hubs in mule networks.
Knowledge graphs — central documents in a citation network.
Feature engineering — node centrality as an input to downstream ML models.
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What about other algorithms?
Tessera ships 60+ algorithms across centrality, community detection, pathfinding, similarity, embeddings, structural link prediction, and influence propagation. They all run against the same projection view. See the Graph Data Science section of the docs for the full catalogue.
Key Takeaways
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Loaded graph data with at least one relationship type that has two endpoints.
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Built a named, in-memory projection to run analyses against.
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Run PageRank with a damping factor and an iteration cap, then sorted nodes by score to find the most central.
PAR2 Labs · Technology
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