neo4j-graph-data-scientist

Name: neo4j-graph-data-scientist
Author: neo4j-contrib

2installs20forks

How to install neo4j-graph-data-scientist?

neo4j-contrib/gds-agent/neo4j-graph-data-scientist

$npx -y skills add neo4j-contrib/gds-agent --skill neo4j-graph-data-scientist

Installs into the current project.

Summary

Best practices for data science work on graphs using the available tools.

Files · 1

View on GitHub

SKILL.md

1	## Workflow
2
3	1. Inspect the database schema first. Never guess labels, types, or property names.
4	2. Project a graph. Plugin and session mode have different projection syntax and parameters. Check the graph projection tool description and parameters.
5	3. Clean up. `drop_graph` when a projection is no longer needed.
6	`delete_session` when a session is no longer needed, and this will automatically drop all graphs projected to this session.
7	4. When you see errors, inspect the message and make necessary corrections. If you cannot fix it, consult the detailed [references/troubleshooting.md](references/troubleshooting.md) guide.
8
9
10	## Best Practices
11	1. Large graphs. When the graph in the DB is large, you might want to consider projecting subgraphs at the start for analysis.
12	When the projected graph is large, consider `mode: "mutate"` to store the computed results in the projected graph and then use `stream_node_properties` or `stream_relationship_properties` to inspect the data.
13	2. Long running tools. Certain algorithms (or Cypher queries) are long running. For exploratory work, consider trying them out on smaller projected graphs before executing them on a desirable large projected graph.
14	3. Follow general data science best practice. Understand if the task is transductive (over the fixed data) or inductive. For predictive tasks, ensure there is no data leakage.
15	Formulate hypothesis and design metrics appropriately. Remember all the basic statistics best practices.
16	4. Perform additional analysis when needed. You do not need to use solely the Cypher and GDS tools.
17	For complex data science task, feel free to use other tools or coding capabilities and write ad-hoc scripts that use other libraries, such as pytorch, scikit-learn, pandas, matplotlib, when necessary.

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neo4j-graph-data-scientist

2installs20forks

View on GitHub

How to install neo4j-graph-data-scientist?

neo4j-contrib/gds-agent/neo4j-graph-data-scientist

$npx -y skills add neo4j-contrib/gds-agent --skill neo4j-graph-data-scientist

Installs into the current project.

Summary

Best practices for data science work on graphs using the available tools.

Files · 1

View on GitHub

SKILL.md

1	## Workflow
2
3	1. Inspect the database schema first. Never guess labels, types, or property names.
4	2. Project a graph. Plugin and session mode have different projection syntax and parameters. Check the graph projection tool description and parameters.
5	3. Clean up. `drop_graph` when a projection is no longer needed.
6	`delete_session` when a session is no longer needed, and this will automatically drop all graphs projected to this session.
7	4. When you see errors, inspect the message and make necessary corrections. If you cannot fix it, consult the detailed [references/troubleshooting.md](references/troubleshooting.md) guide.
8
9
10	## Best Practices
11	1. Large graphs. When the graph in the DB is large, you might want to consider projecting subgraphs at the start for analysis.
12	When the projected graph is large, consider `mode: "mutate"` to store the computed results in the projected graph and then use `stream_node_properties` or `stream_relationship_properties` to inspect the data.
13	2. Long running tools. Certain algorithms (or Cypher queries) are long running. For exploratory work, consider trying them out on smaller projected graphs before executing them on a desirable large projected graph.
14	3. Follow general data science best practice. Understand if the task is transductive (over the fixed data) or inductive. For predictive tasks, ensure there is no data leakage.
15	Formulate hypothesis and design metrics appropriately. Remember all the basic statistics best practices.
16	4. Perform additional analysis when needed. You do not need to use solely the Cypher and GDS tools.
17	For complex data science task, feel free to use other tools or coding capabilities and write ad-hoc scripts that use other libraries, such as pytorch, scikit-learn, pandas, matplotlib, when necessary.

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