Reihaneh Rabbany

hussein.abdallah@mail.concordia.ca

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

Hussein Abdallah

Collaborating researcher - Concordia University

Aurélien Bück-Kaeffer

Master's Research - McGill University

Jacob Chmura

Master's Research - McGill University

Principal supervisor :

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Co-supervisor :

Research Intern - McGill University

Master's Research - McGill University

Victor Livernoche

PhD - McGill University

Sitao Luan

Postdoctorate - McGill University

Principal supervisor :

Guillaume Rabusseau

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

Collaborating researcher - McGill University

Master's Research - McGill University

Website

Soroush Omranpour

Collaborating Alumni - McGill University

Co-supervisor :

Guillaume Rabusseau

kellin.pelrine@mila.quebec

PhD - McGill University

Website

Farimah Poursafaei

Collaborating Alumni - McGill University

Collaborating researcher

Collaborating researcher - McGill University University

Sneheel Sarangi

Collaborating researcher - McGill University

sneheelsarangi@gmail.com

Research Intern - McGill University

jacob.mila.handle@tianshome.com

Vidya Sujaya

Master's Research - McGill University

Camille Thibault

Master's Research - Université de Montréal

Principal supervisor :

Jean-François Godbout

Collaborating researcher - McGill University

Collaborating researcher - Université de Montréal

Principal supervisor :

PhD - McGill University

Jie Zan

Research Intern - McGill University

jie.zan@mail.mcgill.ca

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

Sveta Zhuk

Master's Research - Université de Montréal

Principal supervisor :

Jean-François Godbout

Blog Posts

August 3, 2021

Shenyang Huang

Reihaneh Rabbany

Read the article

Publications

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Yifei Li

Pratheeksha Nair

2022-01-01

Findings (published)

Towards Better Evaluation for Dynamic Link Prediction

Andy Huang

Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In th… (see more)is work, we design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations, to better compare the strengths and weaknesses of methods. First, we create two visualization techniques to understand the reoccurring patterns of edges over time and show that many edges reoccur at later time steps. Based on this observation, we propose a pure memorization-based baseline called EdgeBank. EdgeBank achieves surprisingly strong performance across multiple settings which highlights that the negative edges used in the current evaluation are easy. To sample more challenging negative edges, we introduce two novel negative sampling strategies that improve robustness and better match real-world applications. Lastly, we introduce six new dynamic graph datasets from a diverse set of domains missing from current benchmarks, providing new challenges and opportunities for future research. Our code repository is accessible at https://github.com/fpour/DGB.git.

openreview.net

Curating the Twitter Election Integrity Datasets for Better Online Troll Characterization

Albert Manuel Orozco Camacho

In modern days, social media platforms provide accessible channels for the inter-action and immediate reflection of the most important event… (see more)s happening around the world. In this paper, we, firstly, present a curated set of datasets whose origin stem from the Twitter’s Information Operations efforts. More notably, these accounts, which have been already suspended, provide a notion of how state-backed human trolls operate.Secondly, we present detailed analyses of how these behaviours vary over time,and motivate its use and abstraction in the context of deep representation learning:for instance, to learn and, potentially track, troll behaviour. We present baselinesf or such tasks and highlight the differences there may exist within the literature.Finally, we utilize the representations learned for behaviour prediction to classify trolls from"real"users, using a sample of non-suspended active accounts.

2021-12-07

LatinX in AI at Neural Information Processing Systems Conference 2021 (published)

openreview.net

Online Partisan Polarization of COVID-19

Zachary Yang

Anne Imouza

Sacha Lévy

Jiewen Liu

Gabrielle Desrosiers-Brisebois

Jean-François Godbout

André Blais

In today’s age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on several im… (see more)portant issues, including elections and the COVID-19 pandemic. These two topics have recently become intertwined given the importance of complying with public health measures related to COVID-19 and politicians’ management of the pandemic. Motivated by this, we study the partisan polarization of COVID-19 discussions on social media. We propose and utilize a novel measure of partisan polarization to analyze more than 380 million posts from Twitter and Parler around the 2020 US presidential election. We find strong correlation between peaks in polarization and polarizing events, such as the January 6th Capitol Hill riot. We further classify each post into key COVID-19 issues of lockdown, masks, vaccines, as well as miscellaneous, to investigate both the volume and polarization on these topics and how they vary through time. Parler includes more negative discussions around lockdown and masks, as expected, but not much around vaccines. We also observe more balanced discussions on Twitter and a general disconnect between the discussions on Parler and Twitter.

2021-12-01

2021 International Conference on Data Mining Workshops (ICDMW) (published)

Incorporating dynamic flight network in SEIR to model mobility between populations

Xiaoye Ding

Shenyang Huang

Abby Leung

2021-06-10

Applied Network Science (published)

arxiv.org

The Surprising Performance of Simple Baselines for Misinformation Detection

Jacob Danovitch

As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and preve… (see more)nt the spread of disinformation and unverified rumours. While many sophisticated and successful models have been proposed in the literature, they are often compared with older NLP baselines such as SVMs, CNNs, and LSTMs. In this paper, we examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods. We present our framework as a baseline for creating and evaluating new methods for misinformation detection. We further study a comprehensive set of benchmark datasets, and discuss potential data leakage and the need for careful design of the experiments and understanding of datasets to account for confounding variables. As an extreme case example, we show that classifying only based on the first three digits of tweet ids, which contain information on the date, gives state-of-the-art performance on a commonly used benchmark dataset for fake news detection –Twitter16. We provide a simple tool to detect this problem and suggest steps to mitigate it in future datasets.

2021-06-03

Proceedings of the Web Conference 2021 (published)

arxiv.org

Graph Attention Networks with Positional Embeddings

Liheng Ma

Adriana Romero Soriano

2021-05-09

Advances in Knowledge Discovery and Data Mining (published)

arxiv.org

SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks

Farimah Poursafaei

Željko Žilić

2021-05-09

Advances in Knowledge Discovery and Data Mining (published)

INFOSHIELD: Generalizable Information-Theoretic Human-Trafficking Detection

Meng-Chieh Lee

Catalina Vajiac

Aayushi Kulshrestha

Sacha Lévy

Namyong Park

Cara Jones

Christos Faloutsos

Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking.… (see more) How can we summarize them, visually, to convince law enforcement to act? Can we build a general tool that works for different languages? Spotting micro-clusters of near-duplicate documents is useful in multiple, additional settings, including spam-bot detection in Twitter ads, plagiarism, and more.We present INFOSHIELD, which makes the following contributions: (a) Practical, being scalable and effective on real data, (b) Parameter-free and Principled, requiring no user-defined parameters, (c) Interpretable, finding a document to be the cluster representative, highlighting all the common phrases, and automatically detecting "slots", i.e. phrases that differ in every document; and (d) Generalizable, beating or matching domain-specific methods in Twitter bot detection and human trafficking detection respectively, as well as being language-independent finding clusters in Spanish, Italian, and Japanese. Interpretability is particularly important for the anti human-trafficking domain, where law enforcement must visually inspect ads.Our experiments on real data show that INFOSHIELD correctly identifies Twitter bots with an F1 score over 90% and detects human-trafficking ads with 84% precision. Moreover, it is scalable, requiring about 8 hours for 4 million documents on a stock laptop.

2021-04-19

2021 IEEE 37th International Conference on Data Engineering (ICDE) (published)

RAFFIC V IS : Fighting Human Trafﬁcking through Visualization

Catalina Vajiac

Andreas Olligschlaeger

Yifei Li

Pratheeksha Nair

Meng-Chieh Lee

Namyong Park

Duen Horng Chau

Christos Faloutsos

Law enforcement can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected ads. Given such cl… (see more)usters, how can we interactively visualize potential evidence for law enforcement and domain experts? We present TRAFFICVIS, which, to our knowledge, is the first interface for cluster-level HT detection and labeling. It builds on state-of-the-art HT clustering algorithms by incorporating metadata as a signal of organized and potentially suspicious activity. Also, domain experts can label clusters as HT, spam, and more, efficiently creating labeled datasets to enable further HT research. TRAFFICVIS has been built in close collaboration with domain experts, who estimate that TRAFFICVIS provides a median 36x speedup over manual labeling.

Scalable Change Point Detection for Dynamic Graphs

Shenyang Huang

Guillaume Rabusseau

Real world networks often evolve in complex ways over time. Understanding anomalies in dynamic networks is crucial for applications such as … (see more)traffic accident detection, intrusion identification and detection of ecosystem disturbances. In this work, we focus on the problem of change point detection in dynamic graphs. The goal is to identify time steps where the graph structure deviates significantly from the norm. Despite empirical success of recent methods, building a change point detection method for real world dynamic graphs, which often scale to millions of nodes, remains an open question. To fill this gap, we propose LADdos, a scalable method for change point detection in dynamic graphs. LADdos brings together ideas from two recent works: an accurate change point detection method for graphs called LAD [10] which detects the changes in the full Laplacian spectrum of the graph in each timestamp, and the general framework of network density of states (DOS) [5] which models the distribution of the singular values through efficient approximation methods. In experiments with two common graph models –the Stochastic Block Model (SBM) and the Barabási-Albert (BA) model – we show that LADdos has equal performance to LAD, which is the current state-of-the-art, while being orders of magnitude faster. For instance, on a dynamic graph with total 21 million edges over 150 timestamps, LADdos achieves 100x speedup when compared to LAD.

Graph Neural Networks Learn Twitter Bot Behaviour

Albert Manuel Orozco Camacho

Sacha Lévy

Social media trends are increasingly taking a significant role for the understanding of modern social dynamics. In this work, we take a look… (see more) at how the Twitter landscape gets constantly shaped by automatically generated content. Twitter bot activity can be traced via network abstractions which, we hypothesize, can be learned through state-of-the-art graph neural network techniques. We employ a large bot database, continuously updated by Twitter, to learn how likely is that a user is mentioned by a bot, as well as, for a hashtag. Thus, we model this likelihood as a link prediction task between the set of users and hashtags. Moreover, we contrast our results by performing similar experiments on a crawled data set of real users.

2020-12-12

LatinX in AI at Neural Information Processing Systems Conference 2020 (published)