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Emma Kondrup

emma.kondrup@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Reihaneh Rabbany
Co-supervisor
Catherine Régis
Sujets de recherche
Apprentissage sur graphes
Questions juridiques liées à l'IA
Réseaux de neurones en graphes
Sécurité de l'IA
Site web
Google Scholar
GitHub

Publications

CrediBench: Building Web-Scale Network Datasets for Information Integrity
Emma Kondrup
Sebastian Sabry
Hussein Abdallah
Zachary Yang
James Zhou
Kellin Pelrine
Jean-François Godbout
Michael M. Bronstein
Reihaneh Rabbany
Shenyang Huang
Online misinformation poses an escalating threat, amplified by the Internet's open nature and increasingly capable LLMs that generate persua… (voir plus)sive yet deceptive content. Existing misinformation detection methods typically focus on either textual content or network structure in isolation, failing to leverage the rich, dynamic interplay between website content and hyperlink relationships that characterizes real-world misinformation ecosystems. We introduce CrediBench: a large-scale data processing pipeline for constructing temporal web graphs that jointly model textual content and hyperlink structure for misinformation detection. Unlike prior work, our approach captures the dynamic evolution of general misinformation domains, including changes in both content and inter-site references over time. Our processed one-month snapshot extracted from the Common Crawl archive in December 2024 contains 45 million nodes and 1 billion edges, representing the largest web graph dataset made publicly available for misinformation research to date. From our experiments on this graph snapshot, we demonstrate the strength of both structural and webpage content signals for learning credibility scores, which measure source reliability. The pipeline and experimentation code are all available here, and the dataset is in this folder.
2026-08-07
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (publié)
doi.org
openreview.net
StabilityBench: Benchmarking Instability in LLMs
Emma Kondrup
Zachary Yang
Anne Imouza
Reihaneh Rabbany
AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior re… (voir plus)mains poorly understood due to strong context dependence. Current evaluation protocols follow a defense-in-depth paradigm with compounding layers of safeguards, ranging from traditional benchmarks to live or adversarial testing. Such benchmarks remain largely static and single-turn, limiting their ability to capture real-world variability in conversational settings. We propose StabilityBench, a principled, general and model-agnostic benchmark operator that transforms single-turn benchmark queries into multi-turn interaction histories. StabilityBench augments existing benchmarks by injecting realistic user simulations, through demographic proxies or sycophantic baits, while preserving original task intent. We apply StabilityBench to four benchmarks spanning mathematical reasoning, health question-answering and safety, and evaluate nine large language models under these conditions. Our results show that model performance is consistently unstable under these injections, with considerable performance degradations on three out of four benchmarks studied. These highlight important limitations of static evaluations and motivate more realistic evaluation settings. To this end, we propose StabilityBench-Mini: a size-preserving variant of StabilityBench that samples across diversification axes, enabling more realistic evaluation without increasing costs.
2026-07-16
arXiv (prépublication)
doi.org
arxiv.org
From Claims to Context: Holistic Information Verification Requires Contextual Signals
Emma Kondrup
Islam Eldifrawi
Tong Wu
Zachary Yang
Sebastian Sabry
Michael Bronstein
Jean-François Godbout
Reihaneh Rabbany
Shenyang Huang
2025-12-31
Neural Information Processing Systems (Accept)
openreview.net
openreview.net
Are Large Language Models Good Temporal Graph Learners?
Shenyang Huang
Ali Parviz
Emma Kondrup
Zachary Yang
Zifeng Ding
Michael M. Bronstein
Reihaneh Rabbany
Guillaume Rabusseau
Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. Wh… (voir plus)ile a broad range of literature has explored the graph-reasoning capabilities of LLMs, including their use of predictors on graphs, the application of LLMs to dynamic graphs -- real world evolving networks -- remains relatively unexplored. Recent work studies synthetic temporal graphs generated by random graph models, but applying LLMs to real-world temporal graphs remains an open question. To address this gap, we introduce Temporal Graph Talker (TGTalker), a novel temporal graph learning framework designed for LLMs. TGTalker utilizes the recency bias in temporal graphs to extract relevant structural information, converted to natural language for LLMs, while leveraging temporal neighbors as additional information for prediction. TGTalker demonstrates competitive link prediction capabilities compared to existing Temporal Graph Neural Network (TGNN) models. Across five real-world networks, TGTalker performs competitively with state-of-the-art temporal graph methods while consistently outperforming popular models such as TGN and HTGN. Furthermore, TGTalker generates textual explanations for each prediction, thus opening up exciting new directions in explainability and interpretability for temporal link prediction. The code is publicly available at https://github.com/shenyangHuang/TGTalker.
2025-09-21
NeurIPS.cc/2025/Workshop/NPGML (poster)
doi.org
openreview.net