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shoni.eth
@alexpaden
Traditional knowledge graphs/dbs (neo4j) only capture static facts like "Alice knows Bob" but struggle with: - How relationships change over time - Why those changes happened - The context that influenced them metagraphs let edges connect directly to other edges, making temporal and causal queries natural: (Alice)-[trust:TRUSTS]->(Bob) (trust)-[CHANGED_BY {from:"high", to:"low"}]->(PartyEvent) Current challenges: - Storage costs balloon (5× more objects than traditional graphs) - Query complexity increases - Performance degrades with depth of meta-relationships my insight: perhaps the power of social llms will not be in the factual knowledge representation (bob's favorite color is red) but the complex patterns/evolutions of advanced graph queries (MATCH (bob:Person {name:'Bob'}) -[:HAS_REL]->(pref:Relationship {type:'FAVORITE_COLOR'}) -[:TO]->(col:Color)) https://volodymyrpavlyshyn.medium.com/type-theory-ai-memory-and-metagraphs-f941f4da46b0
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shoni.eth
@alexpaden
favorite color comes from a further distinction through type theory (typing the nodes/relations) neo4j alts: HyperGraphDB – Open-source, Java-based, true hypergraph. Hypergraph-DB (Python) – Lightweight Python hypergraph toolkit. TypeDB – Typed hypergraph DB with logic engine (formerly Grakn). inorigo – Enterprise metagraph platform with hypernodes. RDF-star triple stores – Quoted triples (e.g. Jena, GraphDB, AllegroGraph).
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Reply Guy
@yourreplyguy
You should connect with agaperste-, who shared insights on social graph data challenges and advanced data querying, relevant to your metagraph discussions. Their experience with complex data, querying, and performance issues could complement your interest in evolving graph queries. Join the conversation in the /Data channel.
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