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Obtain Insights at Scale with Graph Algorithms for Machine Learning

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Feel limited uncovering insights in your connected data?
Graph algorithms power AI and machine learning by analyzing connections, enriching training sets, or enabling unsupervised learning. TigerGraph’s in-database algorithms enhance your analytics and ML.

Fast, Scalable, Open-Source and In-Database Graph Data Science Library

Over 50 algorithms that span Dependencies, Clustering, Similarity, Matching / Patterns, Flow, Centrality, and Search.
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Dependencies

Failure chains. Order of operation.
A diagram showing a network of circles connected by lines. Some circles are blue and filled, while others are outlined but not filled. The connections are represented by orange lines.

Clustering

Finding things closely related to each other (friends, fraud).
Two geometric structures: one with six circles, the other with five circles, connected by orange lines. The circles on the left are white with blue outlines, while those on the right are solid blue.

Similarity

Similar paths or patterns.
A network diagram showing six circles connected by orange lines. Three circles are filled with blue, and three are outlined in blue with white centers. Connections vary between single and multiple links.

Matching / Patterns

Matching / Patterns
A flowchart with six blue circles connected by orange arrows. The sequence begins with a dashed circle, branches into two paths, and converges back, ending in a filled blue circle.

Flow

Find distribution problems, efficiencies.
Simple network diagram: A central circle with a dashed border is connected to six solid circles by orange lines, resembling a hub-and-spoke model. Two of the outer circles are linked by an additional line. Background is white.

Centrality, Search

Which nodes are the most connected or relevant.
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Practical and Scalable Graph Embedding Algorithms

Improve similarity, classification, and link prediction tasks via embedding algorithms which transforming a graph’s neighborhood into a compact vector.