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WSDM2022 Challenge - Large scale temporal graph link prediction


Temporal Link Prediction is one of the classical tasks on temporal graphs. Contrary to link prediction which asks if an edge exists between two nodes on a partially observed graph, Temporal Link Prediction asks if an edge will exist between two nodes within a given time span. It is more useful than traditional link prediction as one can then build multiple applications around the model, such as forecasting the demand of customers in E-commerce, or forecasting what event will happen in a social network, etc.

We are expecting an approach that works well on large-scale temporal graphs in general. In this challenge, we expect a single model (hyperparameters can vary) that works well on two kinds of data simultaneously:

The task will be predicting whether an edge of a given type will exist between two given nodes before a given timestamp.

Description of Dataset A

Dataset A contains the following files:

Description of Dataset B

Dataset B contains a single file:

Note that in this dataset the nodes and edge types do not have features, unlike Dataset A.

Test set and submission guidelines

We will release two CSV file input_A.csv and input_B.csv representing the test queries for dataset A and B respectively. Each file contains the following five columns:

We expect two files output_A.csv and output_B.csv representing your predictions on each test query. Each file should contain the same number of lines as the given input files. Each line should contain a single number representing the predicted probability that the edge connecting from node ID src_id to node ID dst_id with type event_type will be added to the graph at some time between start_time and end_time (inclusive of both endpoints).

It is guaranteed that the timestamps in the test set will be always later than the training set. This is to match a more realistic setting where one learns from the past and predicts the future.

During competition we will release an intermediate test set and a final test set. The prizes will only depend on the performance on the final test set, and you will need to submit supplementary materials such as your code repository URL. You can optionally submit your prediction on the intermediate test set and see how your model performs.

11/11 Update:

11/10 Update: We have updated the initial test set for Dataset B so that the nodes not appearing in the training set are removed. The resulting number of test examples is 5,074.

11/4 Update: We have released an initial test set for Dataset A and Dataset B for developing your solutions, as well as a simple baseline.


Say that an edge with type 0 from node 0 to node 1 will appear at timestamp 15000000:

src_id dst_id edge_type timestamp
0 1 0 15000000

You should predict some probability close to 1 for the following query since the timestamp 15000000 is between 14000000 and 16000000:

src_id dst_id edge_type start_time end_time
0 1 0 14000000 16000000

However, you should predict some probability close to 0 for both test queries below:

src_id dst_id edge_type start_time end_time
0 1 0 13000000 14000000
src_id dst_id edge_type start_time end_time
0 1 0 16000000 17000000

Competition Terms and Conditions

At the end of the challenge, each team is encouraged to open source the source code that was used to generate their final challenge solution under the MIT license. To be eligible for the leaderboard or prizes, winning teams are also required to submit papers describing their method to the WSDM Cup Workshop, and present their work at the workshop. Refer to the “Call for Papers” section on the WSDM Cup 2022 webpage for more details.

Participants are allowed to participate only once, with no concurrent submissions or code sharing between the teams. The same team can submit multiple times, with only the last submission being evaluated.

Participants are not allowed to use external datasets or pretrained models.

We welcome any kinds of model in this challenge, regardless of whether it is a deep learning model or some graph learning algorithm.

Evaluation Criteria

We use Area Under ROC (AUC) as evaluation metric for both datasets, and use the harmonic average of the two AUCs as the score of the submission. Specifically, let AUC_A and AUC_B be the AUC for Dataset A and Dataset B respectively, the final score is

2 / (1 / AUC_A + 1 / AUC_B)

This is to encourage the submissions to work well on both tasks, instead of working extremely well on one while sacrificing the other.


Date Event
Oct 15 2021 Website ready and training set available for download.
Nov 11 2021 Intermediate test set release and intermediate submission starts.
Dec 11 2021 Intermediate submission ends.
Dec 16 2021 Intermediate leaderboard result announcement.
Dec 17 2021 Final test set release and final submission starts.
Jan 20 2022 Final submission ends.
Jan 24 2022 Final leaderboard result announcement.
Jan 25 2022 Invitations to top 3 teams for short papers.
Feb 15 2022 Short paper deadline.
Feb 21-25 2022 WSDM Cup conference presentation.


1st place: $2,000 + one WSDM Cup conference registration 2nd place: $1,000 + one WSDM Cup conference registration 3rd place: $500 + one WSDM Cup conference registration

We would like to thank Intel for kindly sponsoring this event.

Support or Contact

If you have questions or need clarifications, feel free to join the channel wsdm22-challenge in DGL’s Slack workspace.

WSDM call for cup proposals: