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Found 13 papers, 9 papers with code
$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
In this work, we propose $\texttt{MiniMol}$, a foundational model for molecular learning with 10 million parameters.
Temporal Graph Analysis with TGX
Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs.
Understanding Opinions Towards Climate Change on Social Media
In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
In this paper, we explore the application of GNNs to edge regression tasks in both static and dynamic settings, focusing on predicting food and agriculture trade values between nations.
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
Current solutions do not scale well to large real-world graphs, lack robustness to large amounts of node additions/deletions, and overlook changes in node attributes.
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction.
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
how to capture temporal dependencies, and iii).
Towards Better Evaluation for Dynamic Link Prediction
To evaluate against more difficult negative edges, we introduce two more challenging negative sampling strategies that improve robustness and better match real-world applications.
Laplacian Change Point Detection for Dynamic Graphs
To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings.
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Almost all neural architecture search methods are evaluated in terms of performance (i. e. test accuracy) of the model structures that it finds.
Neural Architecture Search for Class-incremental Learning
To understand how to expand a continual learner, we focus on the neural architecture design problem in the context of class-incremental learning: at each time step, the learner must optimize its performance on all classes observed so far by selecting the most competitive neural architecture.
