What you’ve learned
You now understand how to work with GDS algorithms at a fundamental level.
Algorithm categories
GDS provides 40+ algorithms across five categories:
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Centrality - Identify important or influential nodes (Degree, PageRank, Betweenness)
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Community Detection - Find natural clusters and groups (Louvain, Leiden, Label Propagation)
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Pathfinding - Discover optimal routes through networks (Dijkstra)
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Similarity - Find structurally similar nodes (Node Similarity, FastRP)
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Embeddings - Create vector representations of nodes (FastRP, GraphSAGE)
Five execution modes
You learned that every algorithm supports five modes:
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Stats - Get summary statistics without individual results
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Stream - View results directly without storing them
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Mutate - Store results in the projection for chaining algorithms
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Write - Persist results back to the database
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Estimate - Check memory requirements before running
Each mode serves a different purpose in your workflow.
Working with algorithm results
You learned to use GDS utility functions to work with algorithm outputs:
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gds.util.asNode()- Convert GDS node IDs to database nodes for property access -
gds.util.nodeProperty()- Access specific properties directly -
gds.util.asNodes()- Convert multiple node IDs at once
These functions are essential when working in stream mode, allowing you to retrieve node names, properties, and other attributes from algorithm results.
Reading GDS documentation
You can now independently learn any GDS algorithm by understanding how to read the documentation:
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Attributes - Check whether the algorithm supports directed, undirected, weighted, or heterogeneous graphs
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Syntax - Find required parameters and modes
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Configuration - Explore customization options
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Examples - Adapt working code for your use cases
Algorithm configuration
You practiced customizing algorithms with both universal and algorithm-specific configurations:
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Universal options:
nodeLabels,relationshipTypes,concurrency -
Orientation options:
undirectedRelationshipTypesfor making relationships bidirectional -
Weight options:
relationshipWeightPropertyto use weighted relationships -
Algorithm-specific options:
dampingFactorfor PageRank,maxLevelsfor Louvain and Leiden
You ran algorithms with different configurations to see how they affect results:
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Directed vs. Undirected: Compared PageRank on directed and undirected actor networks
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Unweighted vs. Weighted: Ran Leiden with and without rating weights
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Configuration tuning: Experimented with various algorithm parameters
The skills you have
You can now:
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Understand what GDS algorithms do and when to use them
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Read GDS documentation independently to learn new algorithms
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Choose the right execution mode for your workflow
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Use utility functions to access node properties from results
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Configure algorithms for directed, undirected, and weighted relationships
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Run complete algorithm workflows from execution to result interpretation
What’s next: Advanced projection techniques
You know how to run and configure algorithms. Next you’ll learn some more advanced techniques for designing projections that enable more sophisticated analyses.
In the next module, Module 4: Advanced Projection Techniques, you’ll learn:
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How to aggregate multiple relationships into weighted connections
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When to use relationship aggregation for performance and insight
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How to design projections based on analytical questions
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How to match projection structure to algorithm requirements
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How to think strategically about projection modeling
By the end of the next module, you’ll be able to independently design and implement complete GDS solutions from analytical question through to projection design, algorithm selection, and result interpretation.
Summary
You’ve mastered the fundamentals of working with GDS algorithms: categories, execution modes, documentation reading, and configuration. You understand how to run any algorithm with appropriate settings for directed, undirected, and weighted relationships.
Module 4 brings together everything you’ve learned about projections and algorithms, teaching you advanced techniques that enable sophisticated graph analysis for real-world problems.