PromptsEdge
FreeGitHubAutomation

Social Graph Ranker

Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.

affaan-m on GitHub

Curated by PromptsEdge from a public repo · MIT license. All credit goes to the author.

What is this skill?

Canonical weighted graph-ranking layer for network-aware outreach.

When to use

  • "who in my network is best positioned to introduce me?"
  • "rank my mutuals by who can get me to these people"
  • "map my graph against this ICP"
  • "show me the bridge math"
  • full lead generation and outbound sequencing -> use lead-intelligence
  • pruning, rebalancing, and growing the network -> use connections-optimizer

Use this when the user needs to:

  • rank existing mutuals or connections by intro value
  • map warm paths to a target list
  • measure bridge value across first- and second-order connections
  • decide which targets deserve warm intros versus direct cold outreach
  • understand the graph math independently from lead-intelligence or connections-optimizer

Inputs

Collect or infer:

  • target people, companies, or ICP definition
  • the user's current graph on X, LinkedIn, or both
  • weighting priorities such as role, industry, geography, and responsiveness
  • traversal depth and decay tolerance

Core Model

Given:

  • T = weighted target set
  • M = your current mutuals / direct connections
  • d(m, t) = shortest hop distance from mutual m to target t
  • w(t) = target weight from signal scoring

Base bridge score:

text
B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)

Where:

  • λ is the decay factor, usually 0.5
  • a direct path contributes full value
  • each extra hop halves the contribution

Second-order expansion:

text
B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))

Where:

  • N(m) \\ M is the set of people the mutual knows that you do not
  • α discounts second-order reach, usually 0.3

Response-adjusted final ranking:

text
R(m) = B_ext(m) · (1 + β · engagement(m))

Where:

  • engagement(m) is normalized responsiveness or relationship strength
  • β is the engagement bonus, usually 0.2

Interpretation:

  • Tier 1: high R(m) and direct bridge paths -> warm intro asks
  • Tier 2: medium R(m) and one-hop bridge paths -> conditional intro asks
  • Tier 3: low R(m) or no viable bridge -> direct outreach or follow-gap fill

Scoring Signals

Weight targets before graph traversal with whatever matters for the current priority set:

  • role or title alignment
  • company or industry fit
  • current activity and recency
  • geographic relevance
  • influence or reach
  • likelihood of response

Weight mutuals after traversal with:

  • number of weighted paths into the target set
  • directness of those paths
  • responsiveness or prior interaction history
  • contextual fit for making the intro

Workflow

  1. Build the weighted target set.
  2. Pull the user's graph from X, LinkedIn, or both.
  3. Compute direct bridge scores.
  4. Expand second-order candidates for the highest-value mutuals.
  5. Rank by R(m).
  6. Return:
    • best warm intro asks
    • conditional bridge paths
    • graph gaps where no warm path exists

Output Shape

text
SOCIAL GRAPH RANKING
====================

Priority Set:
Platforms:
Decay Model:

Top Bridges
- mutual / connection
  base_score:
  extended_score:
  best_targets:
  path_summary:
  recommended_action:

Conditional Paths
- mutual / connection
  reason:
  extra hop cost:

No Warm Path
- target
  recommendation: direct outreach / fill graph gap

Related Skills

  • lead-intelligence uses this ranking model inside the broader target-discovery and outreach pipeline
  • connections-optimizer uses the same bridge logic when deciding who to keep, prune, or add
  • brand-voice should run before drafting any intro request or direct outreach
  • x-api provides X graph access and optional execution paths

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