IEEE

Structure-Aware Model for Representation Learning on Temporal Graphs

Abstract: Temporal graph representation learning seeks to capture the intrinsic evolution of nodes in temporal graphs for various applications. While existing models primarily learn node ...
IEEE

Beyond Graph Structure: Semantic Augmentation with LLMs for Bitcoin Money Laundering Detection under Economic Networks

Abstract: The anonymity of Bitcoin makes transaction tracing challenging, fostering illicit activities such as money laundering. Existing anti-money laundering (AML) approaches based on graph learning ...
GitHub

IRENE: Optimizing EEG Graph Structure for Seizure Detection via Information Bottleneck and Self-Supervised Learning

├── model/ │ ├── GSA_Encoder.py # Structure-aware encoder (Transformer backbone) │ ├── GraphMAE.py # Masked graph autoencoder pretrainer │ ├── IRENE.py # Full IRENE classification model │ ├── ...
Nature

Structure comes to random graphs

Incorporating structural features into random-graph calculations should bring theoretical models describing the properties and behaviour of complex networks closer to real-world systems. You have full ...
GitHub

Geometric Graph U-Nets for Multi-Scale Protein Structure Modelling

Code for the paper "Multi-Scale Protein Structure Modelling with Geometric Graph U-Nets", by Chang Liu*, Vivian Li*, Linus Leong, Vladimir Radenkovic, Pietro Liò, and Chaitanya K. Joshi (*Equal ...