56 citations · 130 across the 16 of their papers we have counts for
12 papers · 1 filter
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…
: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander +5
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data a…
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…
Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
Lekang Jiang, Caiqi Zhang, Farimah Poursafaei +1
Recently, Graph Neural Networks (GNNs) have shown promising performance in tasks on dynamic graphs such as node classification, link prediction and graph regression. However, few w…
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…
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
Dominic Masters, Josef Dean, Kerstin Klaser +9
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing compo…
Towards Better Evaluation for Dynamic Link Prediction
Farimah Poursafaei, Shenyang Huang, Kellin Pelrine +1
Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In this work, we design new, more stringent e…
Laplacian Change Point Detection for Dynamic Graphs
Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau +1
Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is…
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Stefano Alletto, Shenyang Huang, Vincent Francois-Lavet +2
Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a g…
Neural Architecture Search for Class-incremental Learning
Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau
In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur. Existing methods often rely on static architectures that are ma…