newsReddit r/MachineLearningTrust 52 · CommunityPublished 1mo agoLive · 1mo ago
BaryGraph - knowledge graph where every relationship is its own embedded document (not an edge) [R]
Instead of node --edge--> node , every relationship is a first-class document with its own vector, called a BaryEdge. Stack pairs of BaryEdges recursively and you get "MetaBary" triads that surface structural bridges between concepts that live nowhere near each other in embedding space. Running locally on MongoDB Community + mongot + nomic-embed-text over the full English Wiktionary (6.6M docs). MCP server is live if you want to poke at
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 61%Measuring Graph-to-Graph Semantic Similarity in Knowledge Graphs: An Empirical Evaluation of Knowledge Graph Embeddings →
- PossiblePossibly related (embedding) · 51%PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning →
- PossiblePossibly related (embedding) · 48%Grounding LLM Reasoning under Incomplete Graph Evidence →
- PossiblePossibly related (embedding) · 48%Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs →
- PossiblePossibly related (embedding) · 48%yifanfeng97/Hyper-Extract →
- PossiblePossibly related (embedding) · 48%FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs →
- PossiblePossibly related (embedding) · 52%bibinprathap/VeritasGraph →
- PossiblePossibly related (embedding) · 47%neo4j/graph-data-science-client →
Covers
paperMeasuring Graph-to-Graph Semantic Similarity in Knowledge Graphs: An Empirical Evaluation of Knowledge Graph EmbeddingspaperPromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph LearningpaperGrounding LLM Reasoning under Incomplete Graph EvidencepaperEfficient Retrieval-Augmented Generation via Token Co-occurrence Graphsrepoyifanfeng97/Hyper-Extract
Covers (incoming)
paperFedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphsrepobibinprathap/VeritasGraphreponeo4j/graph-data-science-clientrepogrisuno/ReadMenatorpaperConceptual Networks for Cross-Linguistic Idiomatic Expressions:A Feature-Based Graph ApproachrepoGraphify-Labs/graphifyrepolitegraphdb/litegraphrepopykeen/pykeenpaperLION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
Related across the graph
repobibinprathap/VeritasGraphpaperConceptual Networks for Cross-Linguistic Idiomatic Expressions:A Feature-Based Graph ApproachpaperGrounding LLM Reasoning under Incomplete Graph EvidencerepoGraphify-Labs/graphifypaperEfficient Retrieval-Augmented Generation via Token Co-occurrence Graphsrepopykeen/pykeenreponeo4j/graph-data-science-clientpaperLION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learningrepogrisuno/ReadMenatorpaperPromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph LearningpaperMeasuring Graph-to-Graph Semantic Similarity in Knowledge Graphs: An Empirical Evaluation of Knowledge Graph EmbeddingspaperFedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphsrepolitegraphdb/litegraphrepoyifanfeng97/Hyper-Extract