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 - McGill

Julia Gastinger

Collaborateur·rice de recherche - University of Mannheim

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Shenyang Huang

Doctorat - McGill

Co-superviseur⋅e :

Elahe Kooshafar Kooshafar

Maîtrise recherche - McGill

Florence Laflamme

Stagiaire de recherche - UdeM

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

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

Stagiaire de recherche - Université de Montréal

jacob.mila.handle@tianshome.com

Jacob-Junqi Tian

Collaborateur·rice de recherche - McGill

Doctorat - McGill

Sveta Zhuk

Stagiaire de recherche - UdeM

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

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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