---
id: daily-social-graph-ranker
name: "social-graph-ranker"
url: https://skills.yangsir.net/skill/daily-social-graph-ranker
author: affaan-m
domain: sales
tags: ["crm", "sales-automation", "lead-generation", "b2b-sales"]
install_count: 2600
rating: 4.40 (7 reviews)
github: https://github.com/affaan-m/everything-claude-code
---

# social-graph-ranker

> 分析社交图谱并按引介价值排序，识别高价值连接者和最短路径，优化人脉拓展策略

**Stats**: 2,600 installs · 4.4/5 (7 reviews)

## Before / After 对比

### 人脉拓展策略

**Before**:

凭直觉选择联系人，无法评估引介价值，大量无效沟通，转化率低于 5%

**After**:

基于图谱分析识别桥梁节点和高价值路径，精准定位关键决策者，转化率提升至 25%

| Metric | Before | After | Change |
|---|---|---|---|
| 转化率 | 5% | 25% | +400% |

## Readme

# social-graph-ranker

# Social Graph Ranker

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

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`

## When To Use This Standalone

Choose this skill when the user primarily wants the ranking engine:

- "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"

Do not use this by itself when the user really wants:

- full lead generation and outbound sequencing -> use `lead-intelligence`

- pruning, rebalancing, and growing the network -> use `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:

```
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:

```
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:

```
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

- Build the weighted target set.

- Pull the user's graph from X, LinkedIn, or both.

- Compute direct bridge scores.

- Expand second-order candidates for the highest-value mutuals.

- Rank by `R(m)`.

- Return:

best warm intro asks

- conditional bridge paths

- graph gaps where no warm path exists

## Output Shape

```
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

Weekly Installs502Repository[affaan-m/everyt…ude-code](https://github.com/affaan-m/everything-claude-code)GitHub Stars152.8KFirst Seen13 days agoSecurity Audits[Gen Agent Trust HubPass](/affaan-m/everything-claude-code/social-graph-ranker/security/agent-trust-hub)[SocketWarn](/affaan-m/everything-claude-code/social-graph-ranker/security/socket)[SnykWarn](/affaan-m/everything-claude-code/social-graph-ranker/security/snyk)Installed oncodex466opencode445gemini-cli440cursor440antigravity440kimi-cli439

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*Source: https://skills.yangsir.net/skill/daily-social-graph-ranker*
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