Physics-inspired professional network analysis. Three implementations sharing one scoring methodology: mass, velocity, priority. Built to answer which relationships matter most right now.
Professional networks decay silently. The people who can open doors drift away because nobody tracked the relationship momentum. You notice it when you need an introduction and realize you haven't spoken in 18 months. KNIS applies physics to professional relationships: mass (seniority, influence), velocity (recency, interaction frequency), and a blended priority score. The result is a ranked list of who to contact today, not who you connected with in 2019.
Scoring Methodology
Priority=0.6 × Mass+0.4 × Velocity
MassSeniority and influence score. Director-level = high mass. Junior IC = low mass. Role title + company size + industry weight.
VelocityRecency and interaction momentum. Connection 3 weeks ago = high velocity. Connection 3 years ago with no interaction = near zero.
PriorityBlended score. High mass + high velocity = people to contact today. High mass + low velocity = relationships worth reactivating.
Three Implementations
v1 — Standalone
Python Analyzer
Single-file Python tool. Takes LinkedIn CSV export, outputs ranked connection list with mass/velocity/priority scores. matplotlib + pyvis graph visualization.
v2 — Full-stack
SvelteKit + FastAPI
Full platform with persistent SQLite storage, REST API, and SvelteKit frontend. Upload CSV, explore graph interactively, filter by industry and seniority.
v3 — Alpha
Geocoded Maps
Alpha with MapLibre geographic visualization. Geocoded company locations across three tiers (exact, city, country). See where your network is concentrated globally.