Reihaneh Rabbany

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

Jacob Chmura

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice de recherche - University of Mannheim

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Shenyang Huang

Doctorat - McGill

Co-superviseur⋅e :

Maîtrise recherche - McGill

Victor Livernoche

Doctorat - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Doctorat - McGill

Maîtrise recherche - McGill

Soroush Omranpour

Maîtrise recherche - McGill

Co-superviseur⋅e :

Guillaume Rabusseau

kellin.pelrine@mila.quebec

Kellin Pelrine

Doctorat - McGill

Mike Pinder

Collaborateur·rice de recherche

Farimah Poursafaei

Postdoctorat - McGill

Maximilian Puelma Touzel

Collaborateur·rice de recherche

Razieh Razieh Shirzadkhani

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Alexandre St-Aubin

Stagiaire de recherche - McGill

Vidya Sujaya

Maîtrise recherche - McGill

Camille Thibault

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Jean-François Godbout

jacob.mila.handle@tianshome.com

Jacob-Junqi Tian

Collaborateur·rice de recherche - McGill

Doctorat - 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

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Sameer Sardaar

Bill Qi

Alexandre Dionne-Laporte

Guy. A. Rouleau

Yannis Trakadis

2020-02-28

BMC Psychiatry (publié)

doi.org

SGP: Spotting Groups Polluting the Online Political Discourse

Junhao Wang

Sacha Lévy

Ren Wang

Aayushi Kulshrestha

Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information s… (voir plus)pace to confuse and distract voters. It is of paramount importance for social media platforms, users engaged with online political discussions, as well as government agencies to understand the dynamics on social media, and identify malicious groups engaging in misinformation campaigns and thus polluting the general discourse around a topic of interest. Past works to identify such disruptive patterns are mostly focused on analyzing user-generated content such as tweets. In this study, we take a holistic approach and propose SGP to provide an informative birds eye view of all the activities in these social media sites around a broad topic and detect coordinated groups suspicious of engaging in misinformation campaigns. To show the effectiveness of SGP, we deploy it to provide a concise overview of polluting activity on Twitter around the upcoming 2019 Canadian Federal Elections, by analyzing over 60 thousand user accounts connected through 3.4 million connections and 1.3 million hashtags. Users in the polluting groups detected by SGP-flag are over 4x more likely to become suspended while majority of these highly suspicious users detected by SGP-flag escaped Twitter's suspending algorithm. Moreover, while few of the polluting hashtags detected are linked to misinformation campaigns, SGP-sig also flags others that have not been picked up on. More importantly, we also show that a large coordinated set of right-winged conservative groups based in the US are heavily engaged in Canadian politics.

2019-10-16

ArXiv (prépublication)

arxiv.org

Anomaly Detection with Joint Representation Learning of Content and Connection

Junhao Wang

Renhao Wang

Aayushi Kulshrestha

Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information s… (voir plus)pace to confuse and distract voters. Past works to identify disruptive patterns are mostly focused on analyzing the content of tweets. In this study, we jointly embed the information from both user posted content as well as a user's follower network, to detect groups of densely connected users in an unsupervised fashion. We then investigate these dense sub-blocks of users to flag anomalous behavior. In our experiments, we study the tweets related to the upcoming 2019 Canadian Elections, and observe a set of densely-connected users engaging in local politics in different provinces, and exhibiting troll-like behavior.

2019-06-16

ArXiv (prépublication)

arxiv.org

Social-Affiliation Networks: Patterns and the SOAR Model

Dhivya Eswaran

Artur Dubrawski

Christos Faloutsos

2018-09-10

ECML/PKDD (publié)

doi.org

Active Search of Connections for Case Building and Combating Human Trafficking

David Bayani

Artur Dubrawski

How can we help an investigator to efficiently connect the dots and uncover the network of individuals involved in a criminal activity based… (voir plus) on the evidence of their connections, such as visiting the same address, or transacting with the same bank account? We formulate this problem as Active Search of Connections, which finds target entities that share evidence of different types with a given lead, where their relevance to the case is queried interactively from the investigator. We present RedThread, an efficient solution for inferring related and relevant nodes while incorporating the user's feedback to guide the inference. Our experiments focus on case building for combating human trafficking, where the investigator follows leads to expose organized activities, i.e. different escort advertisements that are connected and possibly orchestrated. RedThread is a local algorithm and enables online case building when mining millions of ads posted in one of the largest classified advertising websites. The results of RedThread are interpretable, as they explain how the results are connected to the initial lead. We experimentally show that RedThread learns the importance of the different types and different pieces of evidence, while the former could be transferred between cases.

2018-07-19

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (publié)

doi.org

Modular Networks for Validating Community Detection Algorithms

Justin J Fagnan

Afra Abnar

Osmar R Zaiane

How can we accurately compare different community detection algorithms? These algorithms cluster nodes in a given network, and their perform… (voir plus)ance is often validated on benchmark networks with explicit ground-truth communities. Given the lack of cluster labels in real-world networks, a model that generates realistic networks is required for accurate evaluation of these algorithm. In this paper, we present a simple, intuitive, and flexible benchmark generator to generate intrinsically modular networks for community validation. We show how the generated networks closely comply with the characteristics observed for real networks; whereas their characteristics could be directly controlled to match wide range of real world networks. We further show how common community detection algorithms rank differently when being evaluated on these benchmarks compared to current available alternatives.

2018-01-04

ArXiv (prépublication)

arxiv.org

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