CS697_Assignment_2

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Nov 24, 2024

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CS697 Machine Learning with Graphs - Assignment 2 Submit by the Blackboard System DUE: Friday, Nov 10, 2023, at 11:59 PM • Work submitted after the due date may be graded for correctness, but not credited. • Submit your results and codes. Submissions without codes will not be cred- ited. • The assignment can be found in the attachment (CS697-Assignment-2.ipynb or CS697-Assignment-2.py). It is recommended that you use Google Colab to run and complete the assignment. The .ipynb file includes detailed instruc- tions. In this assignment, we will work to construct our own graph neural network using PyTorch Geometric (PyG) and then apply that model on two Open Graph Benchmark (OGB) datasets. These two datasets will be used to benchmark your model’s performance on two different graph- based tasks: 1) node property prediction, predicting properties of single nodes and 2) graph property prediction, predicting properties of entire graphs or subgraphs. There will be three steps: 1 We will learn how PyTorch Geometric stores graphs as PyTorch tensors. 2 We will load and inspect one of the Open Graph Benchmark (OGB) datasets by using the OGB package. OGB is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. The OGB package not only provides data loaders for each dataset but also model evaluators. 3 We will build our own graph neural network using PyTorch Geometric. We will then train and evaluate our model on the OGB node property prediction and graph property prediction tasks. Required packages: Pytorch, and PyTorch Geometric 1
1 PyTorch Geometric (1 points) • Question 1: What is the number of classes and number of features in the ENZYMES dataset? • Question 2: What is the label of the graph with index 100 in the ENZYMES dataset? • Question 3: How many edges does the graph with index 200 have? • Question 4: How many features are in the ogbn-arxiv graph? 2 GNN: Node Property Prediction (3 points) • Question 5: What are your best model validation and test accuracies? 3 GNN: Graph Property Prediction (3 points) • Question 6: What are your best model validation and test accuracies? 2
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