Portrait de Julia Gastinger

Julia Gastinger

Alumni
Google Scholar

Publications

Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Micheal Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Micheal Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Michael Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger
Shenyang Huang
Mikhail Galkin
Erfan Loghmani
Ali Parviz
Farimah Poursafaei
Jacob Danovitch
Emanuele Rossi
Ioannis Koutis
Heiner Stuckenschmidt
Reihaneh Rabbany
Guillaume Rabusseau
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger
Shenyang Huang
Mikhail Galkin
Erfan Loghmani
Ali Parviz
Farimah Poursafaei
Jacob Danovitch
Emanuele Rossi
Ioannis Koutis
Heiner Stuckenschmidt
Reihaneh Rabbany
Guillaume Rabusseau
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entiti… (voir plus)es over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly larger than existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods.
2024-06-14
ArXiv (prépublication)
doi.org
arxiv.org