Node Classification

(1) Homophily and Heterophily Analysis of Node Classification

  • Find and Download Datasets for Node Classification dataset link
  • Calculate Homophily and Heterophily of different networks
  • Code GNN-based models and obtain node classification performance
  • Analyze the relationship between the homophily of a network/dataset and its corresponding node classification performance

(2) Oversmoothing Analysis of Node Classification

  • Find and Download Datasets for Node Classification dataset link
  • Code GNN-based models and obtain node classification performance
  • Change the Message-passing layer and obtain performance for each layer
  • Analyze the relationship between the number of message-passing layers and the node classification performance

(3) Node Classification on Heterogeneous Graphs

  • Find and Download Heterogeneous Graph Datasets for Node Classification dataset link
  • Code Heterogeneous GNN-based models and obtain node classification performance

(1) Homophily and Heterophily Analysis of Link Prediction

  • Find and Download Datasets for Link Prediction dataset link
  • Calculate Homophily and Heterophily of different networks
  • Code GNN-based models and obtain link prediction performance
  • Analyze the relationship between the homophily of a network/dataset and its corresponding link prediction performance

(2) Degree Analysis of Link Prediction

  • Find and Download Datasets for Link Prediction dataset link
  • Calculate Degree of different nodes within one network/dataset
  • Code GNN-based model and obtain link prediction performance for a specific network
  • Calculate the link prediction performance for each node and group nodes according to their degree in different groups
  • Analyze the relationship between the average group link prediction performance and their average group degree

(3) Implement LightGCN-based Recommender System and Compare its performance on users with different degrees/activity

  • Find and Download Datasets for the Recommender System dataset link
  • Code LightGCN as your GNN-based recommender system and obtain recommendation performance for a specific network
  • Calculate the recommendation performance for each user and group them based on user degree
  • Analyze the relationship between the average group recommendation performance and their average group degree
  • Read materials related to cold-start problems.

(4) Implement LightGCN-based Recommender System and Compare its performance with and without text information

  • Find and Download Datasets for the Recommender System dataset link
  • Code LightGCN as your GNN-based recommender system and obtain recommendation performance for a specific network under two scenarios: with text and without text
  • Calculate the recommendation performance for each user and group them based on user degree
  • Analyze the relationship between the average group recommendation performance and their average group degree
  • Read materials related to cold-start problems.

Edge Classification

(4) Compare Different GNN Models and MLP models on Edge Classification

  • Find and Download Datasets for the Recommender System dataset link
  • Process the review information for each customer-product interaction
  • Code GNN to implement edge classification
  • Calculate the performance and analyze the relationship between the edge classification performance with degree of ending points

Graph Classification

(5) Investigate the Imbalance Issue on Graph Classification

  • Find and Download Datasets for the Graph Classification dataset link
  • Calculate the number of graphs in different classes
  • Code GNN to implement graph classification
  • Change the number of training/validation/testing graphs to create balance and imbalance situations
  • Train the GNN model, obtain graph classification performance on different imbalance ratios, and analyze their relationship.

(6) Investigate whether incorporate 3D coordinates would help Graph Classification

  • Find and Download Datasets for Molecule Classification dataset link
  • Code GNN to implement graph classification for 3D graphs
  • Compare Graph Classification performance with and without 3D coordinates.