Portrait de Alireza Dizaji

Alireza Dizaji

Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e
Guillaume Rabusseau
Sujets de recherche
Apprentissage à quelques exemples
Apprentissage de représentations
Apprentissage multimodal
Apprentissage par renforcement
Grands modèles de langage (LLM)
Modèles génératifs
Raisonnement
Systèmes multi-agents
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Alireza Dizaji
Benedict Aaron Tjandra
Mehrab Hamidi
Shenyang Huang
Guillaume Rabusseau
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite… (voir plus) extensive benchmarking efforts, it remains unclear whether current Temporal Graph Neural Networks (TGNNs) effectively capture core temporal patterns such as periodicity, cause-and-effect, and long-range dependencies. In this work, we introduce the Temporal Graph Reasoning Benchmark (T-GRAB), a comprehensive set of synthetic tasks designed to systematically probe the capabilities of TGNNs to reason across time. T-GRAB provides controlled, interpretable tasks that isolate key temporal skills: counting/memorizing periodic repetitions, inferring delayed causal effects, and capturing long-range dependencies over both spatial and temporal dimensions. We evaluate 11 temporal graph learning methods on these tasks, revealing fundamental shortcomings in their ability to generalize temporal patterns. Our findings offer actionable insights into the limitations of current models, highlight challenges hidden by traditional real-world benchmarks, and motivate the development of architectures with stronger temporal reasoning abilities. The code for T-GRAB can be found at: https://github.com/alirezadizaji/T-GRAB.
2025-06-26
KDD.org/2025/Workshop/MLoG-GenAI (présentation orale)
doi.org
openreview.net