Getting Started

Provisioning a Database Instance

First, we will have to provision a TigerGraph Cloud instance. Once you follow the invite link in the email you recieve from the Google Form, you will see a page like the below:

Click Create Solution in the upper right hand corner. You will then see:

Select Blank v3.6.1 and scroll to the bottom of the page to continue. This will then bring you to the instance configuration page.

Select AWS as the platform, N. Virginia as the region, a Public endpoint, and the TG.C8.M32 Instance Type. Leave the defaults below, scroll to the bottom of the page, and click Next

We will then name and tag the solution we are provisioning. The names, tags, and subdomains must be unique, so choose something that is identifiable to you.

After clicking next, you should see a confirmation page where you can check the details and then hit Submit.

This will then take you back to the solutions page, where you can find your solution after a few minutes:

2. Connect to GraphStudio

Once your solution is provisioned, we want to connect to the GraphStudio UI. To do this, click on the Applications icon and select GraphStudio.

3. Create Graph

This will take you to the GraphStudio landing page, where we can create our graph. Click on the Global View button and select Create a graph:

Clicking this will bring up the following pop-up. Fill in the graph name as KDD_2022_NFT, and then select Create:

After this, you should see the following:

Using ML Workbench

We will use the ML Workbench to perform data analysis and machine learning on the graph data. To do this, we will provision a notebook server with 4 CPU cores and 16 GB of RAM. To start, view the Solutions page on TigerGraph Cloud:

In the upper left hand corner, click on the Tools tab. This will bring you to the following screen:

From there, click on the ML Workbench button:

Once you are on the MLWB homepage, click on Notebooks in the left hand menu bar:

Click on the New Notebook button:

Name your notebook, select the tigergraphml/kf-pytorch:kdd2022 image, and use 4 CPU cores and 16 GB of RAM. Scroll to the bottom of the page and click Launch:

This will take you back to the MLWB notebooks homepage. Click on Connect to connect to the notebook server:

This will take you to the notebook homepage, seen here:

The code for this tutorial will be in the kdd2022-tutorial directory. On the left hand side, click on the Files tab, then select the kdd2022-tutorial directory, and within that select notebooks. Click on 0-load_data.ipynb to open the first notebook. You should see something like this:

In order to connect to your TigerGraph database, we will be using the connection tool on the left hand tab of Jupyter. Click the TigerGraph logo to see a screen similar to this:

Note: If you do not see any solutions, refresh the notebook page and try again.

Select your solution and click Connect:

Copy the code from the pop-up and paste it into the first code cell. This will create the connection to the TigerGraph database.

You will follow the same connection process for the other notebooks.