Portrait of Reihaneh Rabbany

Reihaneh Rabbany

Core Academic Member

reihaneh.rabbany@mila.quebec

Canada CIFAR AI Chair

Assistant Professor, McGill University, School of Computer Science

Research Topics

Data Mining

Graph Neural Networks

Learning on Graphs

Natural Language Processing

Representation Learning

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

Master's Research - McGill University

Principal supervisor :

Nicolas Le Roux

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Julia Gastinger

Collaborating researcher - University of Mannheim

Principal supervisor :

Guillaume Rabusseau

PhD - McGill University

Co-supervisor :

Guillaume Rabusseau

Elahe Kooshafar

Master's Research - McGill University

Victor Livernoche

PhD - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

Pratheeksha Nair

PhD - McGill University

Sahar Omidi Shayegan

Master's Research - McGill University

Soroush Omranpour

Master's Research - McGill University

Co-supervisor :

Guillaume Rabusseau

PhD - McGill University

kellin.pelrine@mila.quebec

Collaborating researcher

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

Master's Research - McGill University

Camille Thibault

Master's Research - Université de Montréal

Principal supervisor :

Jean-François Godbout

Jacob-Junqi Tian

Collaborating researcher - McGill University

jacob.mila.handle@tianshome.com

Collaborating researcher - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

PhD - McGill University

Master's Research - Université de Montréal

Principal supervisor :

Jean-François Godbout

Blog Posts

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

August 3, 2021

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

by

Reihaneh Rabbany

Read the article

Publications

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… (see more)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.