Search Results for author:
Found 4 papers, 1 papers with code
Effective Multi-Graph Neural Networks for Illicit Account Detection on Cryptocurrency Transaction Networks
Extensive experiments, comparing against 14 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently achieves the best performance to accurately detect illicit accounts, while being efficient.
Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder
While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing.
SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection
However, existing graph-level models are generally built on i. i. d.
Large Language Models are In-Context Molecule Learners
Specifically, ICMA incorporates the following three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
