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Found 7 papers, 5 papers with code
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool.
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress.
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
We here present TAPAS, a toolbox of attacks to evaluate synthetic data privacy under a wide range of scenarios.
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study.
Motif-Based Spectral Clustering of Weighted Directed Networks
We conclude that motif-based spectral clustering is a valuable tool for analysis of directed and bipartite weighted networks, which is also scalable and easy to implement.
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball
We present a simple regularization of adversarial perturbations based upon the perceptual loss.
Anomaly Detection in Networks with Application to Financial Transaction Networks
The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks.
Applications Social and Information Networks Physics and Society 05C82
