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lattix

Knowledge graph substrate: core types, algorithms, and serialization formats.

lattix provides three graph representations (KnowledgeGraph, HeteroGraph, HyperGraph), centrality/community algorithms, neighbor sampling for GNN training, and RDF format support.

Usage

[dependencies]
lattix = "0.8.0"

Default features include formats (N-Triples, Turtle, N-Quads, JSON-LD, CSV) and algo (centrality, PageRank, random walks, sampling, label propagation).

use lattix::{Triple, KnowledgeGraph};

let mut kg = KnowledgeGraph::new();
kg.add_triple(Triple::new("Apple", "founded_by", "Steve Jobs"));
kg.add_triple(Triple::new("Apple", "headquartered_in", "Cupertino"));
kg.add_triple(Triple::new("Steve Jobs", "born_in", "San Francisco"));

let apple_relations = kg.relations_from("Apple");
assert_eq!(apple_relations.len(), 2);

Graph types

Type	Purpose	Index structure
`KnowledgeGraph`	Homogeneous triple graph	petgraph + subject/object/predicate indexes
`HeteroGraph`	Typed nodes and edges (PyG-style)	COO + forward/reverse adjacency per edge type
`HyperGraph`	N-ary relations	Qualified triples (Wikidata-style) + hyperedges

HeteroGraph and HyperGraph both convert to/from KnowledgeGraph.

Features

Feature	Default	What it enables
`formats`	yes	N-Triples, Turtle, N-Quads, JSON-LD, CSV (via oxttl/oxrdf)
`algo`	yes	Centrality, PageRank, PPR, random walks, components, sampling, label propagation
`kge`	no	KGE benchmark data: dataset loading, string interning, filtered eval metrics
`binary`	no	postcard serialization (`to_binary_file` / `from_binary_file`)
`sophia`	no	sophia_api 0.10 trait bridge (`Graph`, `MutableGraph`, `CollectibleGraph`)

Disable defaults for core graph types without the formats or algo modules:

lattix = { version = "0.8.0", default-features = false }

For KGE benchmark pipelines (dataset loading, MRR/Hits@k evaluation):

lattix = { version = "0.8.0", default-features = false, features = ["kge"] }

Algorithms

Centrality (7 algorithms): degree, betweenness, closeness, eigenvector, Katz, PageRank, HITS.

Other: personalized PageRank (PPR), Node2Vec-style random walks, connected components, neighbor sampling (homogeneous and heterogeneous), label propagation community detection.

All iterative algorithms have convergence controls (max_iterations, tolerance). Sampling is deterministic under a given seed.

Examples:

cargo run --example triples
cargo run --example pagerank_demo
cargo run --example ppr_retrieval

Formats

Reads and writes N-Triples, Turtle, N-Quads, and JSON-LD. CSV import is read-only. N-Triples parsing uses oxttl, including Triple::from_ntriples.

RDF support is a compatibility layer over lattix triples. IRIs and blank nodes round trip directly; literals are stored in their N-Triples lexical form. RDF 1.2 triple terms are not part of the public graph model.

Dependencies

petgraph is the graph backbone and is re-exported (lattix::petgraph) for advanced use. Algorithm dependencies (rand, rayon, graphops) are optional behind the algo feature. CSV support is optional behind formats; RDF parsing/writing uses oxttl and oxrdf.

License

MIT OR Apache-2.0

lattix 0.8.0