Farimah Poursafaei

Collaborating Alumni - McGill University

Supervisor

Reihaneh Rabbany

Research Topics

Data Mining

Graph Neural Networks

Knowledge Graphs

Learning on Graphs

Recommender Systems

Recurrent Neural Networks

Website

Google Scholar

GitHub

Publications

A Strong Node Classification Baseline for Temporal Graphs

Farimah Poursafaei

Željko Žilić

Reihaneh Rabbany

2022-04-19

Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (published)

doi.org

Towards Better Evaluation for Dynamic Link Prediction

Andy Huang

Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In th… (see more)is work, we design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations, to better compare the strengths and weaknesses of methods. First, we create two visualization techniques to understand the reoccurring patterns of edges over time and show that many edges reoccur at later time steps. Based on this observation, we propose a pure memorization-based baseline called EdgeBank. EdgeBank achieves surprisingly strong performance across multiple settings which highlights that the negative edges used in the current evaluation are easy. To sample more challenging negative edges, we introduce two novel negative sampling strategies that improve robustness and better match real-world applications. Lastly, we introduce six new dynamic graph datasets from a diverse set of domains missing from current benchmarks, providing new challenges and opportunities for future research. Our code repository is accessible at https://github.com/fpour/DGB.git.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)

doi.org

openreview.net

SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks

Farimah Poursafaei

Reihaneh Rabbany

Željko Žilić

2021-05-08

Advances in Knowledge Discovery and Data Mining (published)

doi.org