Opinion Maximization

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Opinion Maximization
Opinion Maximization Based on Fairness in Social Networks

Selects a set of seed nodes in a social graph to maximise the spread of a positive opinion (e.g., endorsement, product recommendation, or RAG system feedback) while ensuring that minority groups receive proportional coverage — not just the well-connected majority. RAG applicability: Influence maximisation governs knowledge propagation in graph-RAG systems (e.g., which documents to prioritise as 'seeds' in a knowledge graph traversal) and agent orchestration (which agents to activate first to maximise information spread). Fairness constraints ensure underrepresented topics receive retrieval coverage. Blue = majority group nodes    Orange = minority group nodes    = selected seed node

## Reference implementation (Vector Ridge Labs, POC-008)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-008
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/opinion_maximization

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures error on held-out data and robustness when the data drifts, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.