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Found 7 papers, 6 papers with code
On the Scalability of GNNs for Molecular Graphs
However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures.
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.
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.
Towards Improved Illicit Node Detection with Positive-Unlabelled Learning
Detecting illicit nodes on blockchain networks is a valuable task for strengthening future regulation.
Active Keyword Selection to Track Evolving Topics on Twitter
How can we study social interactions on evolving topics at a mass scale?
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.
