NodeLink Nexus

Multi-Task Graph Representation Challenge

This challenge explores how well a single graph neural network (GNN) can learn shared node representations that generalize across multiple graph tasks on the same dataset.

Participants must train a model that performs well on both node classification and link prediction, using only the provided graph data and within strict constraints.

View Live Leaderboard

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1. Problem Statement

Given a citation graph where:

• nodes represent research papers,
• edges represent citation relationships, and
• nodes have high-dimensional feature vectors,

your goal is to learn node embeddings that can simultaneously:

1. Classify unseen nodes into research areas
2. Predict unseen edges (citation links) between nodes

The challenge is intentionally designed so that optimizing one task alone is insufficient—successful solutions must learn general-purpose representations useful for multiple objectives.

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2️. Dataset Description

The dataset is derived from a citation network and consists of the following files (located in data/public/):

Node Features

• nodes.csv
• 2708 nodes
• 1433 features per node (x0 to x1432)
• Each row corresponds to a unique node ID

Node Labels

• Training labels: nodes 0–639
• Test labels: nodes 1708–2707
• Nodes 640–1707 are unlabeled and must not be used for node supervision

Graph Structure

• Directed edges represent citations
• The source cites the destination

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3️. Task Definition

This is a multi-task learning challenge with two tasks:

Task 1: Node Classification

Objective:
Predict the research category of unseen nodes.

• Input: node features + graph structure
• Output: class label (integer) from 0 to 6

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Task 2: Link Prediction

Objective:
Predict whether a citation link exists between two nodes.

• Input: pair of node embeddings
• Output: probability ∈ [0, 1]

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Both tasks must be solved using a shared node embedding space.

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4️. Evaluation Metric

Each submission is evaluated on both tasks, and a single final score is computed.

Metrics

• Node Classification: Macro F1-score
• Link Prediction: ROC-AUC

Final Score

• Final Score = 0.5 × Node Macro-F1 + 0.5 × Link ROC-AUC
• Equal weighting ensures that neither task can be ignored.

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5️. Rules & Constraints

To keep the challenge fair and focused:

• No external datasets or pretrained models are allowed
• No manual label engineering
• Any GNN architecture allowed (GCN, GraphSAGE, etc.)
• Solutions should not use different embeddings for both tasks

Submissions that violate these rules may be disqualified.

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6️. How to Submit

1. Fork this repository.

2. Generate predictions for all rows in the public test file:
   data/public/test.csv

3. Create a folder in your fork:

   submissions/inbox/<team_name>/<run_id>/
   

4. Inside that folder, create:

   • predictions.csv
   • metadata.json (optional but recommended)

5. Your predictions.csv must look like:

   id,y_pred
   node_1708,3
   edge_12_45,0.82

   • Node rows → y_pred must be a class label (integer)
   • Edge rows → y_pred must be a probability in [0,1]

6. Encrypt Your Predictions (Required)

   For security and fairness, you must encrypt your submission before uploading.

• Install dependency (if not already installed):

  pip install cryptography

• Encrypt your file from the root of the repository:

  python encryption/encrypt.py submissions/inbox/<team_name>/<run_id>/predictions.csv

  This will generate:

  predictions.csv.enc
  

• Delete the plaintext file:

  rm submissions/inbox/<team_name>/<run_id>/predictions.csv

  Only the encrypted .enc file should be submitted.

7. Your folder should now contain:

   submissions/inbox/<team_name>/<run_id>/
       predictions.csv.enc
       metadata.json
   

8. Submit via Pull Request

   Commit and push only the encrypted file:

   git add submissions/inbox/<team_name>/<run_id>/predictions.csv.enc
   git commit -m "Submission: <team_name>"
   git push

   Then open a Pull Request to this repository.

Important Rules

• Only one submission per GitHub user is allowed.
• If you submit more than once, your PR will not update the leaderboard.
• If your PR does not close automatically, it most likely means:
  • You have already submitted once.
  • Please check the PR comments for details.

Note: If your submission is not scored automatically, it is likely because your GitHub account is considered a first-time or new contributor. In this case, make any prior public contribution on GitHub (e.g., open a PR anywhere, even a typo fix), then re-submit. If the scoring fails, check to make sure your predictions.csv has the correct format, number of rows and columns.

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7️. Leaderboard

The live leaderboard is maintained automatically:

• Scores update instantly after PR submission

View Live Leaderboard

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Getting Started

A simple baseline using a GraphSAGE-style model is provided in baseline.py It demonstrates:

• shared node embeddings
• joint optimization of node + link tasks
• correct submission format

Participants are encouraged to improve upon it. Focus on improving the GNN's learnt features rather than modifying the complete model architecture. A GNN with two MLP heads for prediction as in the baseline should suffice.

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References

• Deep Graph Learning Playlist
• One for All: Towards Training One Graph Model for All Classification Tasks