oxicuda-gnn 0.2.0

Graph Neural Network primitives for OxiCUDA: sparse graph representations (CSR/COO/heterogeneous), message passing framework, GCN/GAT/GraphSAGE/GIN layers, global and hierarchical graph pooling — pure Rust, zero CUDA SDK dependency.
Documentation 
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//! GNN layer implementations.

pub mod appnp;
pub mod chebnet;
pub mod edgeconv;
pub mod gat;
pub mod gat_v2;
pub mod gcn;
pub mod gin;
pub mod grand;
pub mod graph_transformer;
pub mod jk_net;
pub mod k_wl_gnn;
pub mod mixhop;
pub mod norm;
pub mod pna;
pub mod rgcn;
pub mod rwse;
pub mod sage;
pub mod sgc;
pub mod sign;

pub use appnp::{AppnpConfig, AppnpLayer};
pub use chebnet::{ChebNetConfig, ChebNetLayer};
pub use edgeconv::{EdgeConvConfig, EdgeConvLayer, EdgeConvMode, edge_feature};
pub use grand::{GrandConfig, GrandLayer};
pub use graph_transformer::{
    GraphTransformerConfig, GraphTransformerLayer, GraphTransformerWeights,
};
pub use jk_net::{JkMode, JkNet, JkNetConfig};
pub use k_wl_gnn::{KWlConfig, KWlGnn, PairOp, apply_pair_op, graph_readout_sum};
pub use mixhop::{MixHopConfig, MixHopLayer};
pub use norm::{GraphNorm, PairNorm, PairNormMode};
pub use pna::{PnaAggregator, PnaConfig, PnaLayer, PnaScaler, aggregate, scale};
pub use rgcn::{RgcnConfig, RgcnLayer};
pub use rwse::{RwseConfig, RwseEncoder, random_walk_se};
pub use sgc::{sgc_forward, sgc_linear, sgc_propagate};
pub use sign::{SignConfig, SignConv, sign_precompute};