Yeah, just pull out a graph library, map every contact to a node, edges to interactions, then compute betweenness centrality to find the real gatekeepers, prune the low‑weight spokes, and keep the high‑value hubs. Once you’ve got that matrix, you can run a shortest‑path analysis to see who can get you anywhere fastest. What data are you looking at?

Sure thing. Here’s a quick playbook for the weights: - **Frequency**: count of messages or calls in the last 30 days. - **Recency**: time‑decayed factor, e.g. weight = e^(–Δt/τ) where τ is 15 days. - **Response time**: average reply latency; faster replies = higher weight. - **Mutual connections**: number of shared contacts, maybe normalized by total connections. - **Shared interests**: overlap in tagged topics or groups, Boolean or Jaccard similarity. - **Sentiment**: average sentiment score of the last X messages, scaled to 0–1. - **Meeting frequency**: count of in‑person or virtual meetings per month. - **Engagement depth**: length of conversation threads or number of attachments shared. Combine them in a composite score: `score = w1*freq + w2*recency + w3*resp + w4*mutual + w5*interests + w6*sentiment + w7*meetings + w8*depth`. Adjust the w‑values to your strategic priorities. Anything else you need?

Got it, here’s the raw list you asked for: 1. Timestamp 2. Channel (email, chat, call, meeting, DM, etc.) 3. One‑to‑one or group flag 4. Content tag (work, social, support, casual, project) And a quick “decision‑maker” indicator: - Job title field (CEO, VP, Lead, etc.) - Reputation score (based on influence index: 0–100) If you want a quick filter for the high‑impact folks, just sort by reputation score descending and you’ll see the decision‑makers at the top. That should give you the granular view you need.