Farimah Poursafaei

farimah.poursafaei@mila.quebec

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e

Reihaneh Rabbany

Sujets de recherche

Apprentissage sur graphes

Exploration des données

Graphes de connaissances

Réseaux de neurones en graphes

Réseaux de neurones récurrents

Systèmes de recommandation

Site web

Google Scholar

GitHub

Publications

Active Keyword Selection to Track Evolving Topics on Twitter

Sacha Lévy

Farimah Poursafaei

Kellin Pelrine

Reihaneh Rabbany

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as econo… (voir plus)mics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers.

2022-10-31

2022 IEEE International Conference on Data Mining Workshops (ICDMW) (publié)

doi.org

arxiv.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… (voir plus)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.

2022-09-15

NeurIPS.cc/2022/Track/Datasets_and_Benchmarks (accepté)

doi.org

openreview.net

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) (publié)

doi.org

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