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 researcher - McGill University

Julia Gastinger

Collaborating researcher - University of Mannheim

Principal supervisor :

Guillaume Rabusseau

Shenyang Huang

PhD - McGill University

Co-supervisor :

Elahe Kooshafar Kooshafar

Master's Research - McGill University

Florence Laflamme

Research Intern - Université de Montréal

Victor Livernoche

PhD - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

PhD - McGill University

Master's Research - McGill University

Soroush Omranpour

Master's Research - McGill University

Co-supervisor :

Guillaume Rabusseau

kellin.pelrine@mila.quebec

Kellin Pelrine

PhD - McGill University

Farimah Poursafaei

Postdoctorate - McGill University

Maximilian Puelma Touzel

Collaborating researcher

Razieh Razieh Shirzadkhani

Collaborating researcher

Principal supervisor :

Guillaume Rabusseau

Alexandre St-Aubin

Research Intern - McGill University

Vidya Sujaya

Master's Research - McGill University

Camille Thibault

Research Intern - Université de Montréal

jacob.mila.handle@tianshome.com

Jacob-Junqi Tian

Collaborating researcher - McGill University

PhD - McGill University

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

Sveta Zhuk

Research Intern - Université de Montréal

Blog Posts

August 3, 2021

Shenyang Huang

Reihaneh Rabbany

Read the 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… (see more)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 (preprint)

arxiv.org

Social-Affiliation Networks: Patterns and the SOAR Model

Dhivya Eswaran

Artur Dubrawski

Christos Faloutsos

2018-09-10

ECML/PKDD (published)

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… (see more) 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 (published)

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… (see more)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 (preprint)

arxiv.org

PROCLIVITY PATTERNS IN ATTRIBUTED GRAPHS