23 citations · 25 across the 12 of their papers we have counts for
14 papers
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
Ta Thanh Thuy, Jiaqi Zhu, Xuan Liu +6
Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and content moderation. Existing datasets capture…
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal…
RL Fine-Tuning Heals OOD Forgetting in SFT
Hangzhan Jin, Sitao Luan, Tianwei Ni +5
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) is a standard post-training recipe for improving Large Language Models (LLM) reasoning, but why it works remain…
T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Alireza Dizaji, Benedict Aaron Tjandra, Mehrab Hamidi +2
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite extensive benchmarking efforts, it rema…
Are Large Language Models Good Temporal Graph Learners?
Shenyang Huang, Ali Parviz, Emma Kondrup +5
Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. While a broad range of literature has expl…
Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data
Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing mul…
Higher-Order Transformers With Kronecker-Structured Attention
Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
Modern datasets are increasingly high-dimensional and multiway, often represented as tensor-valued data with multi-indexed variables. While Transformers excel in sequence modeling…
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany +2
Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal graphs, either through a stream of edge…
MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs
Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani +7
Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely con…
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger, Shenyang Huang, Mikhail Galkin +9
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel model…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch +7
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning mod…
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau +1
How can we detect traffic disturbances from international flight transportation logs or changes to collaboration dynamics in academic networks? These problems can be formulated as…
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Shenyang Huang, Samy Coulombe, Yasmeen Hitti +2
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for…