Neo4j squashed#10690
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This pull request introduces new database-backed abstractions for large-graph training in PyG, along with a reference Neo4j implementation and documentation. These additions allow PyG to interact with graph topology and features stored in external databases, enabling training on graphs that do not fit in memory and supporting pluggable feature caching. The most important changes are:
New database-backed abstractions and Neo4j implementation:
DatabaseGraphStore,DatabaseFeatureStore, andDatabaseSamplerabstractions for database-backed graph training, with a reference implementation for Neo4j and a runnable Cora notebook underexamples/neo4j.Neo4jFeatureStoreinexamples/neo4j/data/neo4j_feature_store.py, a general implementation ofDatabaseFeatureStorefor Neo4j, supporting configurable attribute mapping, efficient batched queries, and pluggable caching.Documentation and tutorials:
docs/source/tutorial/database_backend.rst) explaining the new database-backed stack, its components, and usage, including how to plug in different databases and cache backends.