Reihaneh Rabbany

Biography

Reihaneh Rabbany is an assistant professor at the School of Computer Science, McGill University, and a core academic member of Mila – Quebec Artificial Intelligence Institute. She is also a Canada CIFAR AI Chair and on the faculty of McGill’s Centre for the Study of Democratic Citizenship.

Before joining McGill, Rabbany was a postdoctoral fellow at the School of Computer Science, Carnegie Mellon University. She completed her PhD in the Department of Computing Science at the University of Alberta.

Rabbany heads McGill’s Complex Data Lab, where she conducts research at the intersection of network science, data mining and machine learning, with a focus on analyzing real-world interconnected data and social good applications.

Current Students

Jacob Chmura

Master's Research - McGill University

Principal supervisor :

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Co-supervisor :

Research Intern - McGill University

Master's Research - McGill University

Victor Livernoche

PhD - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

PhD - McGill University

Master's Research - McGill University

Website

Soroush Omranpour

Master's Research - McGill University

Co-supervisor :

Guillaume Rabusseau

kellin.pelrine@mila.quebec

Kellin Pelrine

PhD - McGill University

Website

Farimah Poursafaei

Postdoctorate - McGill University

Collaborating researcher

Research Intern - McGill University

jacob.mila.handle@tianshome.com

Vidya Sujaya

Master's Research - McGill University

Camille Thibault

Master's Research - Université de Montréal

Principal supervisor :

Jean-François Godbout

Collaborating researcher - McGill University

Collaborating researcher - Université de Montréal

Principal supervisor :

PhD - McGill University

Jie Zan

Research Intern - McGill University

jie.zan@mail.mcgill.ca

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

Sveta Zhuk

Master's Research - Université de Montréal

Principal supervisor :

Jean-François Godbout

Blog Posts

August 3, 2021

Shenyang Huang

Reihaneh Rabbany

Read the article

Publications

TrafficVis: Visualizing Organized Activity and Spatio-Temporal Patterns for Detecting and Labeling Human Trafficking

Catalina Vajiac

Duen Horng Chau

Andreas Olligschlaeger

Rebecca Mackenzie

Pratheeksha Nair

Meng-Chieh Lee

Yifei Li

Namyong Park

Christos Faloutsos

Law enforcement and domain experts can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected… (see more) ads. How can we explain clustering results intuitively and interactively, visualizing potential evidence for experts to analyze? We present TrafficVis, the first interface for cluster-level HT detection and labeling. Developed through months of participatory design with domain experts, TrafficVis provides coordinated views in conjunction with carefully chosen backend algorithms to effectively show spatio-temporal and text patterns to a wide variety of anti-HT stakeholders. We build upon state-of-the-art text clustering algorithms by incorporating shared metadata as a signal of connected and possibly suspicious activity, then visualize the results. Domain experts can use TrafficVis to label clusters as HT, or other, suspicious, but non-HT activity such as spam and scam, quickly creating labeled datasets to enable further HT research. Through domain expert feedback and a usage scenario, we demonstrate TRAFFICVIS's efficacy. The feedback was overwhelmingly positive, with repeated high praises for the usability and explainability of our tool, the latter being vital for indicting possible criminals.

2023-01-01

IEEE Transactions on Visualization and Computer Graphics (published)

Active Keyword Selection to Track Evolving Topics on Twitter

Sacha Lévy

Farimah Poursafaei

Kellin Pelrine

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as econo… (see more)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.

2022-12-01

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

arxiv.org

Early Detection of Sexual Predators with Federated Learning

Khaoula Chehbouni

Gilles Caporossi

Martine De Cock

Golnoosh Farnadi

The rise in screen time and the isolation brought by the different containment measures implemented during the COVID-19 pandemic have led to… (see more) an alarming increase in cases of online grooming. Online grooming is defined as all the strategies used by predators to lure children into sexual exploitation. Previous attempts made in industry and academia on the detection of grooming rely on accessing and monitoring users’ private conversations through the training of a model centrally or by sending personal conversations to a global server. We introduce a first, privacy-preserving, cross-device, federated learning framework for the early detection of sexual predators, which aims to ensure a safe online environment for children while respecting their privacy.

2022-10-20

NeurIPS.cc/2022/Workshop/Federated_Learning (poster)

openreview.net

Revisiting Hotels-50K and Hotel-ID

Aarash Feizi

Arantxa Casanova

Adriana Romero Soriano

In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions prov… (see more)ide evaluation setups with different levels of difﬁculty to better align with the intended real-world application, i.e. countering human trafﬁcking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.

2022-07-20

ArXiv (preprint)

arxiv.org

VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking

Pratheeksha Nair

Yifei Li

Catalina Vajiac

Andreas Olligschlaeger

Meng-Chieh Lee

Namyong Park

Duen Horng Chau

Christos Faloutsos

Chieh Lee

Human trafficking analysts investigate groups of related online escort advertisements (called micro-clusters) to detect suspicious activitie… (see more)s and identify various modus operandi. This task is complex as it requires finding patterns and linked meta-data across micro-clusters such as the geographical spread of ads, cluster sizes, etc. Additionally, drawing insights from the data is challenging without visualizing these micro-clusters. To address this, in close-collaboration with domain experts, we built VisPaD, a novel interactive way for characterizing and visualizing micro-clusters and their associated meta-data, all in one place. VisPaD helps discover underlying patterns in the data by projecting micro-clusters in a lower dimensional space. It also allows the user to select micro-clusters involved in suspicious patterns and interactively examine them leading to faster detection and identification of trends in the data. A demo of VisPaD is also released1.

2022-04-25

The Web Conference (published)

VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking

Pratheeksha Nair

Yifei Li

Catalina Vajiac

Andreas Olligschlaeger

Meng-Chieh Lee

Namyong Park

Duen Horng Chau

Christos Faloutsos

Chieh Lee

2022-04-25

The Web Conference (published)

A Strong Node Classification Baseline for Temporal Graphs

Farimah Poursafaei

Željko Žilić

2022-04-20

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

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Yifei Li

Pratheeksha Nair

Kellin Pelrine

Online escort advertisement websites are widely used for advertising victims of human trafficking. Domain experts agree that advertising mul… (see more)tiple people in the same ad is a strong indicator of trafficking. Thus, extracting person names from the text of these ads can provide valuable clues for further analysis. However, Named-Entity Recognition (NER) on escort ads is challenging because the text can be noisy, colloquial and often lacking proper grammar and punctuation. Most existing state-of-the-art NER models fail to demonstrate satisfactory performance in this task. In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names. It effectively combines classic rule-based and dictionary extractors with a contextualized language model to capture ambiguous names (e.g penny, hazel) and adapts to adversarial changes in the text by expanding its dictionary. NEAT shows 19% improvement on average in the F1 classification score for name extraction compared to previous state-of-the-art in two domain-specific datasets.

2022-01-01

Findings (published)

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Yifei Li

Pratheeksha Nair

Kellin Pelrine

2022-01-01

Findings (published)

Towards Better Evaluation for Dynamic Link Prediction

Farimah Poursafaei

Andy Huang

Shenyang Huang

Kellin Pelrine

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.

openreview.net

Curating the Twitter Election Integrity Datasets for Better Online Troll Characterization

Albert Manuel Orozco Camacho

In modern days, social media platforms provide accessible channels for the inter-action and immediate reflection of the most important event… (see more)s happening around the world. In this paper, we, firstly, present a curated set of datasets whose origin stem from the Twitter’s Information Operations efforts. More notably, these accounts, which have been already suspended, provide a notion of how state-backed human trolls operate.Secondly, we present detailed analyses of how these behaviours vary over time,and motivate its use and abstraction in the context of deep representation learning:for instance, to learn and, potentially track, troll behaviour. We present baselinesf or such tasks and highlight the differences there may exist within the literature.Finally, we utilize the representations learned for behaviour prediction to classify trolls from"real"users, using a sample of non-suspended active accounts.

2021-12-07

LatinX in AI at Neural Information Processing Systems Conference 2021 (published)

openreview.net

Online Partisan Polarization of COVID-19

Zachary Yang

Anne Imouza

Kellin Pelrine

Sacha Lévy

Jiewen Liu

Gabrielle Desrosiers-Brisebois

Jean-François Godbout

André Blais

In today’s age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on several im… (see more)portant issues, including elections and the COVID-19 pandemic. These two topics have recently become intertwined given the importance of complying with public health measures related to COVID-19 and politicians’ management of the pandemic. Motivated by this, we study the partisan polarization of COVID-19 discussions on social media. We propose and utilize a novel measure of partisan polarization to analyze more than 380 million posts from Twitter and Parler around the 2020 US presidential election. We find strong correlation between peaks in polarization and polarizing events, such as the January 6th Capitol Hill riot. We further classify each post into key COVID-19 issues of lockdown, masks, vaccines, as well as miscellaneous, to investigate both the volume and polarization on these topics and how they vary through time. Parler includes more negative discussions around lockdown and masks, as expected, but not much around vaccines. We also observe more balanced discussions on Twitter and a general disconnect between the discussions on Parler and Twitter.

2021-12-01

2021 International Conference on Data Mining Workshops (ICDMW) (published)