TG demo dataset

minhntn · October 9, 2023, 7:13am

Hi,
Can I have the dataset for this demo: this
And also the dataset and code for this demo: this

Thank you!

Jon_Herke · October 9, 2023, 5:08pm

@minhntn I’m still looking for the dataset used in the first link you provided. I did find the schema, queries, and jobs if you would like that.

And also the dataset and code for this demo: this

This is the GitHub project in the second link. It includes a notebook for loading the data into your solution.

github.com

tigergraph/graph-ml-notebooks/blob/main/basics/datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "300b2d48-024f-4d54-8688-8ac2cf7c10e0",
   "metadata": {},
   "source": [
    "# Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122d8f47-a77e-4c70-8bfd-9e1692620ebf",
   "metadata": {},
   "source": [
    "This notebook demostrates how to load two example datasets into your TigerGraph database. It uses [pyTigerGraph](https://github.com/tigergraph/pyTigerGraph) to download the datasets and ingest them into your database. Those datasets will be used throughout the remaining notebooks in the basics directory.\n",
    "\n",
    "The **Cora** dataset contains 2708 machine learning papers and 10556 citation links between the papers.  Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from a dictionary. The dictionary consists of 1433 unique words. Each paper is classified into one of seven classes based on the topic.\n",
    "\n",
    "The **IMDB** dataset contains 3 types of vertices: 4278 movies, 5257 actors, and 2081 directors; and 4 types of edges: 12828 actor to movie edges, 12828 movie to actor edges, 4278 director to movie edges, and 4278 movie to director edges. Each vertex is described by a 0/1-valued word vector indicating the absence/presence of the corresponding keywords. For movies, the keywords are extracted from their plots; and for actors and directors, the keywords are extracted from the plots of movies they participated. Each movie is classified into one of three classes, action, comedy, and drama according to their genre. The goal is to predict the class of each movie in the graph.\n",

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