Portrait de Emma Kondrup

Emma Kondrup

Stagiaire de recherche - McGill
Superviseur⋅e principal⋅e
Reihaneh Rabbany
Sujets de recherche
Apprentissage automatique digne de confiance
Apprentissage sur graphes
Réseaux de neurones en graphes
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.
2025-09-27
ArXiv (prépublication)
arxiv.org
arxiv.org
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.
2025-09-22
NeurIPS.cc/2025/Workshop/NPGML (poster)
doi.org
openreview.net
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.
2025-09-22
NeurIPS.cc/2025/Workshop/NPGML (poster)
doi.org
openreview.net
Are Large Language Models Good Temporal Graph Learners?
Shenyang Huang
Ali Parviz
Emma Kondrup
Zachary Yang
Zifeng Ding
Michael 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.
2025-06-03
ArXiv (prépublication)
arxiv.org
arxiv.org