README.md

WSDM 2022 Large-scale Temporal Graph Link Prediction - Baseline and Initial Test Set

WSDM Cup Website link

Link to this challenge

This branch offers

• An initial test set having a small number of test examples for each dataset, together with their labels in exist column. Note that this test set only serves for development purposes. So
  • The intermediate and final dataset will not contain the exist column.
  • This is not the intermediate dataset we will be using for ranking solutions.
• A simple baseline that trains on both datasets.

Download links to initial test set: Dataset A Dataset B

Baseline description

The baseline is only a minimal working example for both datasets, and it is certainly not optimal. You are encouraged to tweak it or propose your own solutions from scratch!

Here we summarize our baseline: The baseline is an RGCN-like GNN model trained on the entire graph. Event timestamps on the graph are encoded by decomposing the 10-digit decimal integers into 10-dimensional vectors, each element representing a digit. We train the model as binary classification using a negative-sampling-like strategy. Given a ground truth event (s, d, r, t) with source node s, destination node d, event type r and timestamp t, we perturb t to obtain a new value t'. We label the quadruplet with 1 if the new timestamp is larger than the original timestamp, and 0 otherwise. The model is essentially trained to predict p(t < t' | s, d, r), i.e. the probability that an edge with type r exists from source s and destination d before timestamp t'.

Baseline usage

To use the baseline you need to install DGL.

You also need at least 64GB of CPU memory. GPU is not required.

1. Convert csv file to DGL graph objects.

   python csv2DGLgraph.py --dataset [A or B]
   

2. Training.

   python base_pipeline.py --dataset [A or B]
   

Performance on Initial Test Set

The baseline got AUC of 0.511 on Dataset A and 0.510 on Dataset B.