scirs2_graph/ssl/mod.rs
1//! Graph Self-Supervised Learning (SSL) methods.
2//!
3//! Provides contrastive learning and masked autoencoder approaches for
4//! learning graph representations without labels.
5//!
6//! | Sub-module | Method | Reference |
7//! |---|---|---|
8//! | [`contrastive`] | GraphCL, SimGRACE, NT-Xent loss | You et al. 2020; Xia 2022 |
9//! | [`masked_autoencoder`] | GraphMAE with SCE loss | Hou et al. 2022 |
10
11pub mod contrastive;
12pub mod masked_autoencoder;
13/// Graph pre-training strategies: node masking, graph-context contrastive, attribute reconstruction.
14pub mod pretrain;
15
16// Contrastive learning
17pub use contrastive::{
18 augment_edges, augment_features, nt_xent_loss, simgrace_perturb, GraphClConfig, ProjectionHead,
19};
20
21// Masked autoencoder
22pub use masked_autoencoder::{GraphMae, GraphMaeConfig};
23
24// Pre-training strategies
25pub use pretrain::{
26 infonce_loss, AttrReconConfig, AttrReconConfig as AttributeReconConfig,
27 AttributeReconstructionObjective, GraphContextConfig, GraphContextPretrainer,
28 NodeMaskingConfig, NodeMaskingPretrainer,
29};