Online Course Introduction to Graph Algorithms with Neo4j 4.0 Overview of Graph Algorithms Introduction to Graph Data Science library Graph Algorithms Workflow Environment Setup Graph Management Community Detection Algorithms Centrality Algorithms Similarity Algorithms Recipes Analysis Memory Requirements Estimation Additional Information… Read more →

# Summary

### Summary

You should now be able to: Describe what a graph algorithm is and why it is used. Prepare your development environment for using graph algorithms. Write code to implement these types of graph algorithms: Community Detection Centrality Pathfinding Similarity Link Prediction Describe some best practices for using graph algorithms.

### Quiz esse

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Which category of graph algorithms is used to evaluate how a group is clustered or partitioned? Select the correct answer. ❏ Pathfinding ❏ Centrality ✅ Community Detection ❏ Similarity

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Graph algorithm procedures can be called and used the following ways. Select the correct answers. ✅ As a stream procedure that returns a lot of results. ❏ As a stream procedure that returns a lot of results and can update the graph. ✅ As a non-stream procedure that returns statistics. ✅ As a non-stream procedure that returns statistics and can update the graph.

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What Community Detection graph algorithm is useful for creating “sub-graphs” by adding a “partition” property to nodes based upon the neighbors of the node and the weight of the relationship? Select the correct answer. ❏ Connected Components ❏ Strongly Connected Components ❏ Clustering Coefficient ✅ Label Propagation

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