Portrait de Reihaneh Rabbany

Reihaneh Rabbany

Membre académique principal

reihaneh.rabbany@mila.quebec

Chaire en IA Canada-CIFAR

Professeure adjointe, McGill University, École d'informatique

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

Doctorat - McGill University

Co-superviseur⋅e :

Adriana Romero Soriano

feiziaar@mila.quebec

Camille Thibault

Stagiaire de recherche - Université de Montréal

camille.thibault@mila.quebec

Doctorat - McGill University

chin-chen.yang@mila.quebec

Elahe Kooshafar

Maîtrise recherche - McGill University

elahe.kooshafar@mila.quebec

Farimah Poursafaei

Postdoctorat - McGill University

farimah.poursafaei@mila.quebec

Florence Laflamme

Stagiaire de recherche - Université de Montréal

florence.laflamme@mila.quebec

Collaborateur·rice de recherche - McGill University

hao.yu@mila.quebec

Jacob-Junqi Tian

Collaborateur·rice de recherche - McGill University

jacob-junqi.tian@mila.quebec

Jean-François Godbout

Visiteur de recherche indépendant

jean-francois.godbout@mila.quebec

Julia Gastinger

Collaborateur·rice alumni - University of Mannheim

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

julia.gastinger@mila.quebec

Doctorat - McGill University

kellin.pelrine@mila.quebec

Mauricio Rivera

Collaborateur·rice de recherche - Graduated from McGill University

mauricio.rivera@mila.quebec

Pratheeksha Nair

Doctorat - McGill University

pratheeksha.nair@mila.quebec

Shirzadkhani Razieh Shirzadkhani

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

razieh.shirzadkhani@mila.quebec

Sahar Omidi Shayegan

Maîtrise recherche - McGill University

sahar.omidishayegan@mila.quebec

Shahrad Mohammadzadeh

Collaborateur·rice de recherche - McGill University

Co-superviseur⋅e :

shahrad.mohammadzadeh@mila.quebec

Doctorat - McGill University

Co-superviseur⋅e :

Guillaume Rabusseau

huangshe@mila.quebec

Collaborateur·rice de recherche - McGill University

sophia.garrel@mila.quebec

Soroush Omranpour

Maîtrise recherche - McGill University

Co-superviseur⋅e :

Guillaume Rabusseau

soroush.omranpour@mila.quebec

Stagiaire de recherche - Université de Montréal

svetlana.zhuk@mila.quebec

Maîtrise recherche - McGill University

vidya.sujaya@mila.quebec

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

Reihaneh Rabbany

Publications

Active Search of Connections for Case Building and Combating Human Trafficking

Reihaneh Rabbany

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)

Modular Networks for Validating Community Detection Algorithms

Justin J Fagnan

Afra Abnar

Reihaneh Rabbany

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)

PROCLIVITY PATTERNS IN ATTRIBUTED GRAPHS

Reihaneh Rabbany

Dhivya Eswaran

Christos Faloutsos

Artur Dubrawski

Many real world applications include information on both attributes of individual entities as well as relations between them, while there ex… (voir plus)ists an interplay between these attributes and relations. For example, in a typical social network, the similarity of individuals’ characteristics motivates them to form relations, a.k.a. social selection; whereas the characteristics of individuals may be affected by the characteristics of their relations, a.k.a. social influence. We can measure proclivity in networks by quantifying the correlation of nodal attributes and the structure [1]. Here, we are interested in a more fundamental study, to extend the basic statistics defined for graphs and draw parallels for the attributed graphs. More formally, an attributed graph is denoted by (A,X); where An×n is the adjacency matrix and encodes the relationships between the n nodes, and Xn×k is the attributes matrix –each row shows the feature vector of the corresponding node. Degree of a node encodes the number of its neighbors, computed as ki = ∑ j Aij . We can extend this notion to networks with binary attributes to the number of neighbors which share a particular attribute x, i.e. ki(x) = ∑ j Aijδ(Xj , x); where δ(Xj , x) = 1 iff node j has attribute x. Similar to the simple graphs, where the degree distribution is studied and showed to be heavy tail, here we can look at: 1) the degree distributions per attribute, 2) the joint probability distribution of any pair of attributes. Moreover, if we assume A(x1, x2) is the induced subgraph (or masked matrix of edges) with endpoints of values (x1, x2), i.e., A(x1, x2) = Aijδ(Xi, x1)δ(Xj , x2), then we can study and compare these distributions for the induced subgraph per each pair of attribute values. For example, Figure 1 shows the same general trend in the distribution of the original graph and the three possible induced subgraph.

2017-01-01

(publié)

www.semanticscholar.org