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shoni.eth
@alexpaden
>-- The 15th AI Research Group Call Summary (05/20) --< > 🧠 Minimalist Task Management with Markdown Concept: A streamlined, daily note-taking interface powered by a single Markdown file. - Features: - Automatic extraction of entities and tasks into a two-column layout. - Unfinished tasks persist until completion, providing a "cold start" solution. > 📊 Graph-Based Community Analytics (Quotient) - Reputation Graph: - Built on Farcaster social data using Neo4j. - Edge Weighting: Assigns higher weights to replies over recasts and likes. - Temporal Decay: Recent interactions are weighted more heavily. - Seeded PageRank: Traversals initiate from trusted nodes to emphasize meaningful influence and mitigate spam. - Graph Machine Learning: - Graph Projection: Extracts subgraphs (~117k nodes, 1.1M edges) for efficient analytics.
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shoni.eth
@alexpaden
- FastRP Embeddings: Generates node embeddings akin to node2vec, with potential integration of text embeddings (e.g., bios, posts). - Applications: - Identifying similar accounts. - Recommending nodes beyond immediate network vicinity. - Discovering analogous influencers across communities. - Predicting links and properties using K-nearest neighbors queries. > 🧠 AI Integration and Hallucination Mitigation - Structured Knowledge Graphs: - Serve as a factual backbone for AI agents, reducing hallucinations and adversarial prompt injections. - Enable reliable AI responses grounded in curated data. > 🏗️ Data Architecture and Performance - Hybrid Approach: - Maintains authoritative schema and provenance in relational databases (e.g., Postgres). - Loads pertinent subgraphs into Neo4j for analytics. - Performance Optimization:
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shoni.eth
@alexpaden
- Separates full and incremental data ingestion to avoid locks. - Filters projections by credibility/activity to enhance graph algorithm efficiency. > 🚀 Future Directions - Multi-Source Data Ingestion: - Plans to incorporate additional data sources, including on-chain events, governance actions, and off-chain web/social data (e.g., GitHub, LinkedIn). - Advanced Graph Structures: - Exploring hypergraphs/metagraphs to represent complex relationships, especially in conjunction with LLMs. https://flur.ee https://en.wikipedia.org/wiki/Resource_Description_Framework https://en.wikipedia.org/wiki/SPARQL https://en.wikipedia.org/wiki/Semantic_triple
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