neo4j_streamlit's Introduction

neo4j_streamlit

Written by: Dr. Clair J. Sullivan, Data Science Advocate, Neo4j

email: [email protected]

Twitter: @CJLovesData1

Last updated: 2021-06-10

Introduction

The purpose of this code is to demonstrate how to create a Streamlit dashboard to be used for the visualization of embeddings created by the Graph Data Science library in Neo4j. This will hopefully be useful for developing an intuitive feel for these embeddings and how the different hyperparameters impact the overall results.

For more information on graph embeddings, you can read my blog post on Getting Started with Graph Embeddings in Neo4j in Towards Data Science (written in May, 2021).

How to get started

Create a Neo4j Sandbox

Select "Launch a Free Instance"
Select "New Project"
Choose "Graph Data Science": This will create an instance pre-populated with the Game of Thrones graph we will use to get started
Choose the drop-down on the right of the instance and select "Connection details"
Record the Bolt URL and Password

Build the Streamlit container

Edit the Dockerfile to include the Bolt URL and password from the previous step
From the root directory of this repo, type docker build -t neo_stream .
Once built, type docker run -p 8501:8501 -v $PWD/src:/examples neo_stream

Using a browser, navigate to the provided IP address

This typically will be something like http://172.17.0.2:8501

In the side panel, provide a name of the in-memory graph to be created and click "Create in-memory graph"
Have fun with the functionality in the main area of the dashboard! How well can you tune the hyperparameters to get as much clustering separation between the living (blue) and the dead (red) characters???

Note: If you would like, you can navigate to https://bolt.URL.address:7474 and you can interact with the graph directly via Cypher.

Major caveats!!!

This is very much a work in progress! As such, there are only two types of graph embeddings included (FastRP and node2vec) and not all of the hyperparameters have been added yet. Be sure to watch for future modifications and regularly pull the main branch of this repo to keep with the current version.
The in-memory graph that is created is only the undirected, monopartite graph (Person)-[:INTERACTS]-(Person). Future versions of this code will allow you to create additional graphs.
This is a very small graph! It only has about 2600 nodes and not quite 17,000 relationships. And we make that even smaller by limiting the nodes and relationships as described above. Based on these facts, we do not expect the quality of the embeddings to be that great. In future work, we will use a larger graph and discuss how to optimize embeddings.

neo4j_streamlit's People

Contributors

Stargazers

Watchers

neo4j_streamlit's Issues

No response from Streamlit

Hi CJ, I am following your post and get to the point of running the docker image:

$ docker run -p 8501:8501 -v $PWD/src:/examples neo_stream
2021-11-28 22:57:42.325 override steps (5) and chunk_size (512) as content does not fit (14 byte(s) given) parameters.
2021-11-28 22:57:42.330 Trying to detect encoding from a tiny portion of (14) byte(s).
2021-11-28 22:57:42.363 ascii passed initial chaos probing. Mean measured chaos is 0.000000 %
2021-11-28 22:57:42.369 ascii should target any language(s) of ['Latin Based']
2021-11-28 22:57:42.369 ascii is most likely the one. Stopping the process.

  You can now view your Streamlit app in your browser.

  Network URL: http://172.17.0.2:8501

However, I get no response at the above URL. Any thoughts?
PS: I cloned the repo to WSL Ubuntu.

Thanks John

Recommend Projects