social-graph-ranker

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Social Graph Ranker

社交图排名器

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
```

用于网络感知型外展的标准加权图排名层。

当用户需要以下功能时可使用本工具：

按介绍价值对现有共同联系人或直接联系人进行排名
绘制通往目标列表的暖路径
衡量一阶和二阶联系人之间的桥梁价值
决定哪些目标适合暖介绍，哪些适合直接冷外展
独立于
```
lead-intelligence
```
或
```
connections-optimizer
```
理解图运算逻辑

When To Use This Standalone

何时单独使用本工具

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
```

当用户主要需要排名引擎时选择本技能：

"我的网络中谁最适合为我做介绍？"
"按谁能帮我联系到这些人的能力对我的共同联系人进行排名"
"将我的社交图与这个理想客户画像（ICP）进行匹配"
"向我展示桥梁运算的数学逻辑"

当用户实际需要以下功能时，请勿单独使用本工具：

完整的线索生成和外展序列编排 -> 使用
```
lead-intelligence
```
网络的修剪、重新平衡和拓展 -> 使用
```
connections-optimizer
```

Inputs

输入信息

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

收集或推断以下信息：

目标人物、公司或理想客户画像（ICP）定义
用户在X、LinkedIn或两者平台上的当前社交图
权重优先级，如职位、行业、地域和响应度
遍历深度和衰减容忍度

Core Model

核心模型

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

已知：

```
T
```
= 加权目标集合
```
M
```
= 用户当前的共同联系人/直接联系人
```
d(m, t)
```
= 共同联系人
```
m
```
到目标
```
t
```
的最短跳数距离
```
w(t)
```
= 基于信号评分的目标权重

基础桥梁评分：

text

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

其中：

```
λ
```
为衰减因子，通常取值
```
0.5
```
直接路径贡献全部价值
每多一跳，贡献值减半

二阶扩展：

text

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

其中：

```
N(m) \\ M
```
是共同联系人认识但用户不认识的人群集合
```
α
```
为二阶触达的折扣系数，通常取值
```
0.3
```

响应度调整后的最终排名：

text

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

其中：

```
engagement(m)
```
为标准化的响应度或关系强度
```
β
```
为互动奖励系数，通常取值
```
0.2
```

解读：

一级：高
```
R(m)
```
值且有直接桥梁路径 -> 请求暖介绍
二级：中等
```
R(m)
```
值且有一跳桥梁路径 -> 有条件地请求介绍
三级：低
```
R(m)
```
值或无可行桥梁路径 -> 直接外展或填补网络差距

Scoring Signals

评分信号

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

在图遍历前，根据当前优先级集合对目标进行加权，可考虑以下因素：

职位或头衔匹配度
公司或行业契合度
当前活跃度和近期互动情况
地域相关性
影响力或覆盖范围
响应可能性

在图遍历后，对共同联系人进行加权，可考虑以下因素：

通往目标集合的加权路径数量
路径的直接性
响应度或过往互动历史
进行介绍的场景适配性

Workflow

工作流程

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

构建加权目标集合。
从X、LinkedIn或两者平台获取用户的社交图。
计算直接桥梁评分。
为最高价值的共同联系人扩展二阶候选对象。
按
```
R(m)
```
值进行排名。
返回结果：
- 最佳暖介绍请求对象
- 有条件的桥梁路径
- 无暖路径存在的网络差距

Output Shape

输出格式

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

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

social-graph-ranker

Original

Translation

Social Graph Ranker

社交图排名器

When To Use This Standalone

何时单独使用本工具

Inputs

输入信息

Core Model

核心模型

Scoring Signals

评分信号

Workflow

工作流程

Output Shape

输出格式

Related Skills

相关技能