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
Website
Google Scholar
LinkedIn

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 :
Nicolas Le Roux
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Aarash Feizi
PhD - McGill University
Co-supervisor :
Adriana Romero Soriano
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Sophia Garrel
Collaborating researcher - McGill University
Github
Julia Gastinger
Collaborating researcher - University of Mannheim
Principal supervisor :
Guillaume Rabusseau
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Jean-François Godbout
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Shenyang Huang
PhD - McGill University
Co-supervisor :
Guillaume Rabusseau
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Github
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Elahe Kooshafar
Master's Research - McGill University
Florence Laflamme
Research Intern - Université de Montréal
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Victor Livernoche
PhD - McGill University
Github
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Shahrad Mohammadzadeh
Master's Research - McGill University
Co-supervisor :
Doina Precup
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Pratheeksha Nair
PhD - McGill University
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Sahar Omidi Shayegan
Master's Research - McGill University
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Soroush Omranpour
Master's Research - McGill University
Co-supervisor :
Guillaume Rabusseau
Github
Kellin Pelrine
PhD - McGill University
kellin.pelrine@mila.quebec
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Farimah Poursafaei
Postdoctorate - McGill University
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Maximilian Puelma Touzel
Collaborating researcher
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Razieh Razieh Shirzadkhani
Collaborating researcher
Principal supervisor :
Guillaume Rabusseau
Github
Alexandre St-Aubin
Research Intern - McGill University
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Vidya Sujaya
Master's Research - McGill University
Camille Thibault
Research Intern - Université de Montréal
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Jacob-Junqi Tian
Collaborating researcher - McGill University
jacob.mila.handle@tianshome.com
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Zachary Yang
PhD - McGill University
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Sveta Zhuk
Research Intern - Université de Montréal

Publications

Social-Affiliation Networks: Patterns and the SOAR Model
Dhivya Eswaran
Reihaneh Rabbany
Artur Dubrawski
Christos Faloutsos
2018-09-10
ECML/PKDD (published)
doi.org
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… (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
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… (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
arxiv.org
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.
2017-01-01
(published)
www.semanticscholar.org