Reihaneh Rabbany

Membre académique principal

reihaneh.rabbany@mila.quebec

Chaire en IA Canada-CIFAR

Professeure adjointe, McGill University, École d'informatique

Sujets de recherche

Apprentissage de représentations

Apprentissage sur graphes

Exploration des données

Réseaux de neurones en graphes

Traitement du langage naturel

Site web

Google Scholar

Biographie

Reihaneh Rabbany est professeure adjointe à l'École d'informatique de l'Université McGill. Elle est membre du corps professoral de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Elle est également membre du corps enseignant du Centre pour l’étude de la citoyenneté démocratique de McGill. Avant de se joindre à l’Université McGill, elle a été boursière postdoctorale à la School of Computer Science de l'Université Carnegie Mellon. Elle a obtenu un doctorat à l’Université de l’Alberta, au Département d'informatique. Elle dirige le laboratoire de données complexes, dont les recherches se situent à l'intersection de la science des réseaux, de l'exploration des données et de l'apprentissage automatique, et se concentrent sur l'analyse des données interconnectées du monde réel et sur les applications sociales.

Étudiants actuels

Hussein Abdallah

Collaborateur·rice de recherche - Concordia

Github

Aurélien Bück-Kaeffer

Maîtrise recherche - McGill

Github

Jacob Chmura

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Stagiaire de recherche - McGill

Doctorat - McGill

Sitao Luan

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Github

Google Scholar

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Collaborateur·rice de recherche - McGill

Maîtrise recherche - McGill

Site web

Github

Soroush Omranpour

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Guillaume Rabusseau

Github

Farimah Poursafaei

Collaborateur·rice alumni - McGill

Collaborateur·rice de recherche

Collaborateur·rice de recherche - McGill University

Github

Sneheel Sarangi

Collaborateur·rice de recherche - McGill

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Jean-François Godbout

Collaborateur·rice de recherche - McGill

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - McGill

Jie Zan

Stagiaire de recherche - McGill

Sveta Zhuk

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Jean-François Godbout

Billets de blogue

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

3 août 2021

Flight-SEIR : incorporer les données de vol pour améliorer la modélisation épidémiologique et la prévention d’éclosions de maladies infectieuses

par

Shenyang Huang

Reihaneh Rabbany

Lire l'article

Publications

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs

Tran Gia Bao Ngo

Jure Leskovec

Michael M. Bronstein

Guillaume Rabusseau

Matthias Fey

Reihaneh Rabbany

Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like Py… (voir plus)Torch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over time, lacks comparable infrastructure. Existing TG libraries are often tailored to specific architectures, hindering support for diverse models in this rapidly evolving field. Additionally, the divide between continuous- and discrete-time dynamic graph methods (CTDG and DTDG) limits direct comparisons and idea transfer. To address these gaps, we introduce Temporal Graph Modelling (TGM), a research-oriented library for ML on temporal graphs, the first to unify CTDG and DTDG approaches. TGM offers first-class support for dynamic node features, time-granularity conversions, and native handling of link-, node-, and graph-level tasks. Empirically, TGM achieves an average 7.8x speedup across multiple models, datasets, and tasks compared to the widely used DyGLib, and an average 175x speedup on graph discretization relative to available implementations. Beyond efficiency, we show in our experiments how TGM unlocks entirely new research possibilities by enabling dynamic graph property prediction and time-driven training paradigms, opening the door to questions previously impractical to study. TGM is available at https://github.com/tgm-team/tgm

2025-10-08

ArXiv (prépublication)

arxiv.org

CrediBench: Building Web-Scale Network Datasets for Information Integrity

James Zhou

Jean-François Godbout

Michael M. Bronstein

Reihaneh Rabbany

Shenyang Huang

Online misinformation poses an escalating threat, amplified by the Internet's open nature and increasingly capable LLMs that generate persua… (voir plus)sive yet deceptive content. Existing misinformation detection methods typically focus on either textual content or network structure in isolation, failing to leverage the rich, dynamic interplay between website content and hyperlink relationships that characterizes real-world misinformation ecosystems. We introduce CrediBench: a large-scale data processing pipeline for constructing temporal web graphs that jointly model textual content and hyperlink structure for misinformation detection. Unlike prior work, our approach captures the dynamic evolution of general misinformation domains, including changes in both content and inter-site references over time. Our processed one-month snapshot extracted from the Common Crawl archive in December 2024 contains 45 million nodes and 1 billion edges, representing the largest web graph dataset made publicly available for misinformation research to date. From our experiments on this graph snapshot, we demonstrate the strength of both structural and webpage content signals for learning credibility scores, which measure source reliability. The pipeline and experimentation code are all available here, and the dataset is in this folder.

2025-09-27

ArXiv (prépublication)

arxiv.org

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection

Akshatha Arodi

Ga'etan Marceau Caron

Jean-François Godbout

Reihaneh Rabbany

Deepfakes, synthetic media created using advanced AI techniques, have intensified the spread of misinformation, particularly in politically … (voir plus)sensitive contexts. Existing deepfake detection datasets are often limited, relying on outdated generation methods, low realism, or single-face imagery, restricting the effectiveness for general synthetic image detection. By analyzing social media posts, we identify multiple modalities through which deepfakes propagate misinformation. Furthermore, our human perception study demonstrates that recently developed proprietary models produce synthetic images increasingly indistinguishable from real ones, complicating accurate identification by the general public. Consequently, we present a comprehensive, politically-focused dataset specifically crafted for benchmarking detection against modern generative models. This dataset contains three million real images paired with descriptive captions, which are used for generating 963k corresponding high-quality synthetic images from a mix of proprietary and open-source models. Recognizing the continual evolution of generative techniques, we introduce an innovative crowdsourced adversarial platform, where participants are incentivized to generate and submit challenging synthetic images. This ongoing community-driven initiative ensures that deepfake detection methods remain robust and adaptive, proactively safeguarding public discourse from sophisticated misinformation threats.

2025-09-11

ArXiv (prépublication)

arxiv.org

RL Fine-Tuning Heals OOD Forgetting in SFT

Hangzhan Jin

Sicheng Lyu

Mohammad Hamdaqa

2025-09-08

ArXiv (prépublication)

arxiv.org

RL Fine-Tuning Heals OOD Forgetting in SFT

Hangzhan Jin

Sicheng Lyu

Mohammad Hamdaqa

The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reas… (voir plus)oning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. In our study, we find the well-known claim"SFT memorizes, RL generalizes"is over-simplified, and discover that: (1) OOD performance peaks at the early stage of SFT and then declines (OOD forgetting), the best SFT checkpoint cannot be captured by training/test loss; (2) the subsequent RL stage does not generate fundamentally better OOD capability, instead it plays an \textbf{OOD restoration} role, recovering the lost reasoning ability during SFT; (3) The recovery ability has boundaries, \ie{} \textbf{if SFT trains for too short or too long, RL cannot recover the lost OOD ability;} (4) To uncover the underlying mechanisms behind the forgetting and restoration process, we employ SVD analysis on parameter matrices, manually edit them, and observe their impacts on model performance. Unlike the common belief that the shift of model capacity mainly results from the changes of singular values, we find that they are actually quite stable throughout fine-tuning. Instead, the OOD behavior strongly correlates with the \textbf{rotation of singular vectors}. Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism. %reversing the rotations induced by SFT, which shows recovery from forgetting, whereas imposing the SFT parameter directions onto a RL-tuned model results in performance degradation. Code is available at https://github.com/xiaodanguoguo/RL_Heals_SFT

2025-09-08

ArXiv (prépublication)

arxiv.org

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

Taylor Lynn Curtis

James Zhou

Florence Laflamme

Yuxiang Guan

Reihaneh Rabbany

Jean-François Godbout

Kellin Pelrine

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this probl… (voir plus)em, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims, as well as the 9 datasets that consists of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at [anonymized].

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

doi.org

openreview.net

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

Taylor Lynn Curtis

James Zhou

Florence Laflamme

Luke Yuxiang Guan