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Single-partition graph JEPAs bias representations toward one structural granularity. HP-JEPA learns at multiple resolutions simultaneously — outperforming fixed-resolution baselines on 6 of 8 benchmarks.
Force-directed placement scaled to 4 million nodes, 34 million edges in under 10 seconds with 72% less memory. The secret: negative sampling, not multi-level representations.
GraphRAG has a representation-inference gap that breaks complex queries. ACE-GraphRAG adds an agent layer that adapts context dynamically — here's how it works and why it matters.
Graph Functional Dependency validation used to require HPC clusters. FastGFDs runs 3x faster on consumer PCs with 5x less memory — and it's open source.
Distributed graph processing is the thinnest cell in graph research — Pregel lineage, trillion-edge systems, when distributed is the wrong tool.
Natural-language-to-graph-query is the fastest-moving cell in graph research: GQLBench, dialect diversity, agentic tooling, production expectations.