Reihaneh Rabbany

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

Jacob Chmura

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice de recherche - McGill

Julia Gastinger

Collaborateur·rice de recherche - University of Mannheim

Superviseur⋅e principal⋅e :

Shenyang Huang

Doctorat - McGill

Co-superviseur⋅e :

Elahe Kooshafar Kooshafar

Maîtrise recherche - McGill

Florence Laflamme

Stagiaire de recherche - UdeM

Doctorat - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Doctorat - McGill

Maîtrise recherche - McGill

Soroush Omranpour

Maîtrise recherche - McGill

Co-superviseur⋅e :

kellin.pelrine@mila.quebec

Kellin Pelrine

Doctorat - McGill

Farimah Poursafaei

Postdoctorat - McGill

Maximilian Puelma Touzel

Collaborateur·rice de recherche

Razieh Razieh Shirzadkhani

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Alexandre St-Aubin

Stagiaire de recherche - McGill

Vidya Sujaya

Maîtrise recherche - McGill

Camille Thibault

Stagiaire de recherche - Université de Montréal

jacob.mila.handle@tianshome.com

Jacob-Junqi Tian

Collaborateur·rice de recherche - McGill

Doctorat - McGill

Sveta Zhuk

Stagiaire de recherche - UdeM

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

Shenyang Huang

Reihaneh Rabbany

Lire l'article

Publications

The Surprising Performance of Simple Baselines for Misinformation Detection

Kellin Pelrine

Jacob Danovitch

As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and preve… (voir plus)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 (publié)

Graph Attention Networks with Positional Embeddings

Liheng Ma

Adriana Romero Soriano

2021-05-09

Advances in Knowledge Discovery and Data Mining (publié)

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 (publié)

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.… (voir plus) 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) (publié)

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… (voir plus)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.

2021-01-01

(publié)

www.semanticscholar.org

Scalable Change Point Detection for Dynamic Graphs

Shenyang Huang

Real world networks often evolve in complex ways over time. Understanding anomalies in dynamic networks is crucial for applications such as … (voir plus)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.

2021-01-01

(publié)

www.semanticscholar.org

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… (voir plus) 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 (publié)

ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents

Kellin Pelrine

Jacob Danovitch

Albert Orozco Camacho

Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present … (voir plus)Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.

2020-11-01

WNUT (publié)

Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies

Abby Leung

Xiaoye Ding

Shenyang Huang

The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that t… (voir plus)he structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening.

2020-10-06

ArXiv (prépublication)

Laplacian Change Point Detection for Dynamic Graphs

Shenyang Huang

Yasmeen Hitti

2020-08-20

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (publié)

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Sameer Sardaar

Bill Qi

Alexandre Dionne-Laporte

Guy. A. Rouleau

Yannis Trakadis

2020-02-28

BMC Psychiatry (publié)

SGP: Spotting Groups Polluting the Online Political Discourse

Junhao Wang

Sacha Lévy

Ren 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… (voir plus)pace to confuse and distract voters. It is of paramount importance for social media platforms, users engaged with online political discussions, as well as government agencies to understand the dynamics on social media, and identify malicious groups engaging in misinformation campaigns and thus polluting the general discourse around a topic of interest. Past works to identify such disruptive patterns are mostly focused on analyzing user-generated content such as tweets. In this study, we take a holistic approach and propose SGP to provide an informative birds eye view of all the activities in these social media sites around a broad topic and detect coordinated groups suspicious of engaging in misinformation campaigns. To show the effectiveness of SGP, we deploy it to provide a concise overview of polluting activity on Twitter around the upcoming 2019 Canadian Federal Elections, by analyzing over 60 thousand user accounts connected through 3.4 million connections and 1.3 million hashtags. Users in the polluting groups detected by SGP-flag are over 4x more likely to become suspended while majority of these highly suspicious users detected by SGP-flag escaped Twitter's suspending algorithm. Moreover, while few of the polluting hashtags detected are linked to misinformation campaigns, SGP-sig also flags others that have not been picked up on. More importantly, we also show that a large coordinated set of right-winged conservative groups based in the US are heavily engaged in Canadian politics.

2019-10-16

ArXiv (prépublication)