Live Tech Knowledge Graph

Core Philosophy: Beyond a Static Map

Our goal is to build a self-adaptive “tech brain” that:

Breathes: Continuously ingests multi-modal data—from papers, code, news, social, funding, and patents.
Learns: Discerns influence, momentum, outliers, and cross-domain cascades.
Predicts: Anticipates the birth of new fields, convergence points, and “weak signals” before they go mainstream.
Adapts: Evolves its own schema, relationships, and coverage as the tech ecosystem shifts.

Level 1: Atomic Entities (Dynamic Nodes)

This is the “ground truth” layer—what is directly discoverable in the wild—enriched with real-time and predictive metadata.

Entity	Description	Example(s)	Dynamic Attributes
Post	Social/blog/news/forum post	arXiv preprint, @karpathy tweet	`velocity`, `virality`, `reach`
Event	Conference, hackathon, challenge, webinar	NeurIPS 2025, Hugging Face Space	`attendance`, `trend_delta`
Researcher	Individual author/contributor	Yann LeCun, Chelsea Finn	`influence_score`, `field_shift`
Institution	University, company, research org	DeepMind, MIT, Stability AI	`innovation_rate`, `talent_influx`
Media Platform	Platform hosting content	arXiv, Twitter, Hacker News	`activity_index`, `topic_heatmap`
Social Media	Specific accounts, communities, feeds	@OpenAI, /r/MachineLearning	`engagement_delta`, `topic_momentum`
Blog	Tech or research blog	Distill.pub, EleutherAI blog	`post_frequency`, `influence_trend`
Patent	Registered patent	US Patent 12345678	`citation_acceleration`, `category_evolution`
Technology	Concept, model, tool, standard, method	LoRA, Qiskit, RLHF	`maturity_stage`, `adoption_surge`
Funding Source	Org/fund/grant supporting work	NSF, Horizon Europe, Schmidt Futures	`funding_flow`, `focus_shift`
Code Repository	Source code base or dataset repo	github.com/openai/gpt-2, HF repo	`star_velocity`, `fork_trend`

Level 2: Thematic Areas (Fields, Topics, Fusion Zones)

This layer encodes the “macro” landscape—domain, subdomain, and emergent topic clusters. It’s hierarchical, extensible, and allows one entity to be linked to many topics.

A. Established Domains

Artificial Intelligence
- Foundation Models
- AI Safety & Alignment
- AI Agents & Automation
- Explainable AI
- Generative AI
Quantum Technologies
- Quantum ML
- Quantum Hardware
Robotics
- Swarm Robotics
- Embodied AI
Bioinformatics / Computational Biology
Edge Computing & AIoT
Smart Home / Smart Cities

B. Fusion Zones / Interdisciplinary Hotspots

AI + BioTech (Protein Design, Drug Discovery)
AI + Robotics (Foundation Models for Embodied AI)
AI + Security (Privacy, Federated Learning)
EnergyTech (AI for Energy Grids)
Brain-Computer Interfaces
Tech Policy & Governance

C. Weak Signals / Wildcards

Photonic AI Chips
Quantum Internet
Neuromorphic Computing
AI for Space Exploration
Synthetic Biology

D. Custom/Extended Topics

New topics can be added dynamically as signals emerge (e.g., “Post-LLM Era AI”, “AI for Law”, etc.).

Level 3: Relationships (Dynamic Edges and Provenance)

Defines both classic and dynamic links—allowing the graph to capture not just structure but evolution, causality, and prediction.

Semantic (“Structural”) Edges

(Researcher)-[:AUTHORED]->(Post/Publication)
(Researcher)-[:WORKS_AT]->(Institution)
(Work)-[:HOSTED_ON]->(Platform)
(Technology)-[:CATEGORIZED_AS]->(Thematic Area)
(Patent)-[:ASSOCIATED_WITH]->(Technology)
(Code Repository)-[:MENTIONS]->(Technology)
(Event)-[:FOCUSES_ON]->(Topic)

Dynamic & Temporal Edges

(Researcher)-[:COLLABORATES_WITH {since, intensity}]->(Researcher)
(Technology)-[:DERIVED_FROM]->(Technology)
(Event)-[:TRIGGERS]->(Post)
(Field)-[:CONVERGING_WITH {probability, signals}]->(Field)
(Patent)-[:CITED_BY]->(Post/Publication)
(Funding Source)-[:FUNDS]->(Institution/Work)

Predictive/Influence Edges

(TechA)-[:LIKELY_TO_IMPACT {confidence:0.9, timescale:12mo}]->(TechB)
(Person)-[:EMERGING_IN]->(Topic)
(Topic)-[:PREDICTED_TO_MERGE_WITH {prob:0.85}]->(Topic)

Provenance/Discovery Edges

(Node)-[:DISCOVERED_BY {agent, date, method, signal_strength}]->(DiscoveryAgent)
(Node)-[:ENRICHED_BY]->(LLMAgent)

Automated Graph Enrichment & Evolution

Pulse Monitoring: Scheduled jobs or event-driven agents scan new data (arXiv, GitHub, Twitter, Crunchbase, etc.) to enrich entities and create new nodes/edges.
Anomaly & Signal Detection: Graph analytics/ML/LLMs highlight sudden changes, outliers, or emerging clusters for human or agentic review.
Trend and Convergence Prediction: Agentic or LLM-powered modules analyze graph patterns to recommend new topics, flag rising stars, or forecast likely convergences.
Self-Organization: Includes automatic topic/tag assignment for new entities, dynamic subgraph creation for newly identified fusion zones, and schema evolution as new entity/relationship types appear.

Sample Query Types Enabled by the Framework

Static: “List all code repositories for Quantum Machine Learning trending in the last 6 months.”
Temporal: “Show top 10 institutions by innovation_velocity in Edge AI over the last year.”
Predictive: “Which fusion zones are most likely to spawn new conferences within 2 years?”
Cascade: “Trace the influence network from the LoRA paper to current open-source projects and patents.”
Gap Analysis: “What subtopics in AI Safety have low publication_density but rising funding_flow?”

Visualization and Interaction

Multi-level graph exploration: Users/agents can zoom from macro (domain) to micro (entity) with dynamic overlays for velocity, novelty, influence, and prediction.
Time sliders: Watch topics/entities “rise and fall” in the graph over time.
Alerting: Notify when certain signals (velocity, emergence) cross user-defined thresholds.

Summary Table: Framework Features

Layer	Description	Enrichment/Automation
Entities (L1)	Concrete, time-aware nodes	Real-time update, signal analysis, auto-tag
Topics (L2)	Flexible, hierarchical macro structure	Dynamic topic assignment, clustering, fusion
Relationships (L3)	Static, dynamic, predictive, provenance	Auto-link, causal inference, LLM enrichment
Intelligence	Agents for pulse, anomaly, prediction	Active, scheduled, or event-driven updates

The Live Tech Knowledge Graph