LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs
LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs explores RAMP redefines message passing in text-rich graphs by using LLMs as graph-native aggregation operators.. Commercial viability score: 3/10 in Graph Learning.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
MVP Investment
$9K - $13K
6-10 weeksEngineering
$8,000GPU Compute
$800SaaS Stack
$300Domain & Legal
$1006mo ROI
0.5-1x
3yr ROI
6-15x
GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.
Talent Scout
Find Builders
Graph experts on LinkedIn & GitHub
Related Papers
- Builds On ThisRiemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph IntelligenceViability: 2.0
- Higher ViabilityToward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction TuningViability: 6.0
- Higher ViabilityLLMs can construct powerful representations and streamline sample-efficient supervised learningViability: 7.0
- Higher ViabilityHELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAGViability: 6.0
- Higher Viability