# Financial-Knowledge-Graphs
**Repository Path**: bqw_5189/Financial-Knowledge-Graphs
## Basic Information
- **Project Name**: Financial-Knowledge-Graphs
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-04
- **Last Updated**: 2024-07-04
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## 小型金融知识图谱构建示例
 
- [1. 知识图谱存储方式](#1-知识图谱存储方式)
- [1.1 资源描述框架特性](#11-资源描述框架特性)
- [1.2 图数据库特性](#12-图数据库特性)
- [2. 图数据库neo4j](#2-图数据库neo4j)
- [2.1 软件下载](#21-软件下载)
- [2.2 启动登录](#22-启动登录)
- [2.2.1 Windows](#221-windows)
- [2.2.2 MacOS](#222-macos)
- [2.3 储备知识](#23-储备知识)
- [2.4 Windows安装时可能遇到问题及解决方法](#24-windows安装时可能遇到问题及解决方法)
- [3. 知识图谱数据准备](#3-知识图谱数据准备)
- [3.1 免费开源金融数据接口](#31-免费开源金融数据接口)
- [3.1.1 Tushare](#311-tushare)
- [3.1.2 JointQuant](#312-jointquant)
- [3.1.3 导入模块](#313-导入模块)
- [3.2 数据预处理](#32-数据预处理)
- [3.2.1 股票基本信息](#321-股票基本信息)
- [3.2.2 股票持有股东信息](#322-股票持有股东信息)
- [3.2.3 股票概念信息](#323-股票概念信息)
- [3.2.4 股票公告信息](#324-股票公告信息)
- [3.2.5 财经新闻信息](#325-财经新闻信息)
- [3.2.6 概念信息](#326-概念信息)
- [3.2.7 沪股通和深股通成分信息](#327-沪股通和深股通成分信息)
- [3.2.8 股票价格信息](#328-股票价格信息)
- [3.2.9 使用免费接口获取股票数据](#329-使用免费接口获取股票数据)
- [3.3 数据预处理](#33-数据预处理)
- [3.3.1 统计股票的交易日量众数](#331-统计股票的交易日量众数)
- [3.3.2 计算股票对数收益](#332-计算股票对数收益)
- [3.3.3 股票间对数收益率相关系数](#333-股票间对数收益率相关系数)
- [4 搭建金融知识图谱](#4-搭建金融知识图谱)
- [4.1 基于python连接](#41-基于python连接)
- [4.2 读取数据](#42-读取数据)
- [4.3 填充和去重](#43-填充和去重)
- [4.4 创建实体](#44-创建实体)
- [4.5 创建关系](#45-创建关系)
- [5 数据可视化查询](#5-数据可视化查询)
- [5.1 查看所有关联实体](#51-查看所有关联实体)
- [5.2 限制显示数量](#52-限制显示数量)
- [5.3 指定股票间对数收益率相关系数](#53-指定股票间对数收益率相关系数)
- [6 neo4j 图算法](#6-neo4j-图算法)
- [6.1.中心度算法(Centralities)](#61中心度算法centralities)
- [6.2 社区检测算法(Community detection)](#62-社区检测算法community-detection)
- [6.3 路径搜索算法(Path finding)](#63-路径搜索算法path-finding)
- [6.4 相似性算法(Similarity)](#64-相似性算法similarity)
- [6.5 链接预测(Link Prediction)](#65-链接预测link-prediction)
- [6.6 预处理算法(Preprocessing)](#66-预处理算法preprocessing)
- [6.7 算法库安装及导入方法](#67-算法库安装及导入方法)
- [6.8 算法实践——链路预测](#68-算法实践链路预测)
- [6.8.1 Aaamic Adar algorithm](#681-aaamic-adar-algorithm)
- [6.8.2 Common Neighbors](#682-common-neighbors)
- [6.8.3 Resource Allocation](#683-resource-allocation)
- [6.8.4 Total Neighbors](#684-total-neighbors)
## 1. 知识图谱存储方式
知识图谱存储方式主要包含资源描述框架([Resource Description Framework，RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework))和图数据库（[Graph Database](https://en.wikipedia.org/wiki/Graph_database)）。
### 1.1 资源描述框架特性
- 存储为三元组（Triple）
- 标准的推理引擎
- [W3C标准](https://en.wikipedia.org/wiki/World_Wide_Web_Consortium)
- 易于发布数据
- 多数为学术界场景
### 1.2 图数据库特性
- 节点和关系均可以包含属性
- 没有标准的推理引擎
- 图的遍历效率高
- 事务管理
- 多数为工业界场景
---
## 2. 图数据库neo4j
neo4j是一款[NoSQL](https://en.wikipedia.org/wiki/NoSQL)图数据库，具备高性能的读写可扩展性，基于高效的图形查询语言`Cypher`，更多介绍可访问[neo4j官网](https://neo4j.com/)，官网还提供了[Online Sandbox](https://neo4j.com/sandbox/)实现快速上手体验。
### 2.1 软件下载
下载链接：https://neo4j.com/download-center/
### 2.2 启动登录
#### 2.2.1 Windows
- 进入`neo4j`目录
```
cd neo4j/bin
./neo4j start
```
- 启动成功，终端出现如下提示即为启动成功
```
Starting Neo4j.Started neo4j (pid 30914). It is available at http://localhost:7474/ There may be a short delay until the server is ready.
```
（1）访问页面：http://localhost:7474
（2）初始账户和密码均为`neo4j`（`host`类型选择`bolt`）
（3）输入旧密码并输入新密码：启动前注意本地已安装`jdk`（建议安装`jdk version 11`）：https://www.oracle.com/java/technologies/javase-downloads.html
#### 2.2.2 MacOS
执行 Add Local DBMS 后，再打开 Neo4j Browser即可
### 2.3 储备知识
在 neo4j 上执行 CRUD 时需要使用 Cypher 查询语言。
- [官网文档](https://neo4j.com/developer/cypher/)
- [个人整理的常见Cypher指令](https://github.com/jm199504/Financial-Knowledge-Graphs/tree/master/cypher%20cheetsheet)
### 2.4 Windows安装时可能遇到问题及解决方法
- 问题：完成安装JDK1.8.0_261后，在启动`neo4j`过程中出现了以下问题：
```
Unable to find any JVMs matching version "11"
```
- 解决方案：提示安装`jdk 11 version`，于是下载了`jdk-11.0.8`，`Mac OS`可通过`ls -la /Library/Java/JavaVirtualMachines/`查看已安装的`jdk`及版本信息。
---
## 3. 知识图谱数据准备
### 3.1 免费开源金融数据接口
Tushare免费账号可能无法拉取数据，可参考issues提供的股票数据获取方法: https://github.com/jm199504/Financial-Knowledge-Graphs/issues/2#issuecomment-801732782
#### 3.1.1 Tushare
官网链接：http://www.tushare.org/
#### 3.1.2 JointQuant
官网链接：https://www.joinquant.com/
#### 3.1.3 导入模块
```python
import tushare as ts # 参考Tushare官网提供的安装方式
import csv
import time
import pandas as pd
# 以下pro_api token可能已过期，可自行前往申请或者使用免费版本
pro = ts.pro_api('4340a981b3102106757287c11833fc14e310c4bacf8275f067c9b82d')
```
### 3.2 数据预处理
#### 3.2.1 股票基本信息
```python
stock_basic = pro.stock_basic(list_status='L', fields='ts_code, symbol, name, industry')
# 重命名行，便于后面导入neo4j
basic_rename = {'ts_code': 'TS代码', 'symbol': '股票代码', 'name': '股票名称', 'industry': '行业'}
stock_basic.rename(columns=basic_rename, inplace=True)
# 保存为stock_basic.csv
stock_basic.to_csv('financial_data\\stock_basic.csv', encoding='gbk')
```
#### 3.2.2 股票持有股东信息
```python
holders = pd.DataFrame(columns=('ts_code', 'ann_date', 'end_date', 'holder_name', 'hold_amount', 'hold_ratio'))
# 获取一年内所有上市股票股东信息（可以获取一个报告期的）
for i in range(3610):
code = stock_basic['TS代码'].values[i]
holders = pro.top10_holders(ts_code=code, start_date='20180101', end_date='20181231')
holders = holders.append(holders)
if i % 600 == 0:
print(i)
time.sleep(0.4)# 数据接口限制
# 保存为stock_holders.csv
holders.to_csv('financial_data\\stock_holders.csv', encoding='gbk')
holders = pro.holders(ts_code='000001.SZ', start_date='20180101', end_date='20181231')
```
#### 3.2.3 股票概念信息
```python
concept_details = pd.DataFrame(columns=('id', 'concept_name', 'ts_code', 'name'))
for i in range(358):
id = 'TS' + str(i)
concept_detail = pro.concept_detail(id=id)
concept_details = concept_details.append(concept_detail)
time.sleep(0.4)
# 保存为concept_detail.csv
concept_details.to_csv('financial_data\\stock_concept.csv', encoding='gbk')
```
#### 3.2.4 股票公告信息
```python
for i in range(3610):
code = stock_basic['TS代码'].values[i]
notices = pro.anns(ts_code=code, start_date='20180101', end_date='20181231', year='2018')
notices.to_csv("financial_data\\notices\\"+str(code)+".csv",encoding='utf_8_sig',index=False)
notices = pro.anns(ts_code='000001.SZ', start_date='20180101', end_date='20181231', year='2018')
```
#### 3.2.5 财经新闻信息
```python
news = pro.news(src='sina', start_date='20180101', end_date='20181231')
news.to_csv("financial_data\\news.csv",encoding='utf_8_sig')
```
#### 3.2.6 概念信息
```python
concept = pro.concept()
concept.to_csv('financial_data\\concept.csv', encoding='gbk')
```
#### 3.2.7 沪股通和深股通成分信息
```python
#获取沪股通成分
sh = pro.hs_const(hs_type='SH')
sh.to_csv("financial_data\\sh.csv",index=False)
#获取深股通成分
sz = pro.hs_const(hs_type='SZ')
sz.to_csv("financial_data\\sz.csv",index=False)
```
#### 3.2.8 股票价格信息
```python
for i in range(3610):
code = stock_basic['TS代码'].values[i]
price = pro.query('daily', ts_code=code, start_date='20180101', end_date='20181231')
price.to_csv("financial_data\\price\\"+str(code)+".csv",index=False)
```
#### 3.2.9 使用免费接口获取股票数据
```python
import tushare as ts
# 基本面信息
df = ts.get_stock_basics()
# 公告信息
ts.get_notices("000001")
# 新浪股吧
ts.guba_sina()
# 历史价格数据
ts.get_hist_data("000001")
# 历史价格数据（周粒度）
ts.get_hist_data("000001",ktype="w")
# 历史价格数据（1分钟粒度）
ts.get_hist_data("000001",ktype="m")
# 历史价格数据（5分钟粒度）
ts.get_hist_data("000001",ktype="5")
# 指数数据（sh上证指数;sz深圳成指;hs300沪深300;sz50上证50;zxb中小板指数;cyb创业板指数）
ts.get_hist_data("cyb")
# 宏观数据(居民消费指数)
ts.get_cpi()
# 获取分笔数据
ts.get_tick_data('000001', date='2018-10-08', src='tt')
```
### 3.3 数据预处理
#### 3.3.1 统计股票的交易日量众数
```python
import numpy as np
yaxis = list()
for i in listdir:
stock = pd.read_csv("financial_data\\price_logreturn\\"+i)
yaxis.append(len(stock['logreturn']))
counts = np.bincount(yaxis)
np.argmax(counts)
```
#### 3.3.2 计算股票对数收益
股票对数收益及皮尔逊相关系数的计算公式：
```python
import pandas as pd
import numpy as np
import os
import math
listdir = os.listdir("financial_data\\price")
for l in listdir:
stock = pd.read_csv('financial_data\\price\\'+l)
stock['index'] = [1]* len(stock['close'])
stock['next_close'] = stock.groupby('index')['close'].shift(-1)
stock = stock.drop(index=stock.index[-1])
logreturn = list()
for i in stock.index:
logreturn.append(math.log(stock['next_close'][i]/stock['close'][i]))
stock['logreturn'] = logreturn
stock.to_csv("financial_data\\price_logreturn\\"+l,index=False)
```
#### 3.3.3 股票间对数收益率相关系数
```python
from math import sqrt
def multipl(a,b):
sumofab=0.0
for i in range(len(a)):
temp=a[i]*b[i]
sumofab+=temp
return sumofab
def corrcoef(x,y):
n=len(x)
#求和
sum1=sum(x)
sum2=sum(y)
#求乘积之和
sumofxy=multipl(x,y)
#求平方和
sumofx2 = sum([pow(i,2) for i in x])
sumofy2 = sum([pow(j,2) for j in y])
num=sumofxy-(float(sum1)*float(sum2)/n)
#计算皮尔逊相关系数
den=sqrt((sumofx2-float(sum1**2)/n)*(sumofy2-float(sum2**2)/n))
return num/den
```
由于原始数据达百万条，为节省计算量仅选取前300个股票进行关联性分析
```python
listdir = os.listdir("financial_data\\300stock_logreturn")
s1 = list()
s2 = list()
corr = list()
for i in listdir:
for j in listdir:
stocka = pd.read_csv("financial_data\\300stock_logreturn\\"+i)
stockb = pd.read_csv("financial_data\\300stock_logreturn\\"+j)
if len(stocka['logreturn']) == 242 and len(stockb['logreturn']) == 242:
s1.append(str(i)[:10])
s2.append(str(j)[:10])
corr.append(corrcoef(stocka['logreturn'],stockb['logreturn']))
print(str(i)[:10],str(j)[:10],corrcoef(stocka['logreturn'],stockb['logreturn']))
corrdf = pd.DataFrame()
corrdf['s1'] = s1
corrdf['s2'] = s2
corrdf['corr'] = corr
corrdf.to_csv("financial_data\\corr.csv")
```
## 4 搭建金融知识图谱
安装第三方库
```shell
pip install py2neo
```
### 4.1 基于python连接
具体代码可参考3.1 python操作neo4j-连接
```python
from pandas import DataFrame
from py2neo import Graph,Node,Relationship,NodeMatcher
import pandas as pd
import numpy as np
import os
# 连接Neo4j数据库
graph = Graph('http://localhost:7474/db/data/',username='neo4j',password='neo4j')
```
### 4.2 读取数据
```python
stock = pd.read_csv('stock_basic.csv',encoding="gbk")
holder = pd.read_csv('holders.csv')
concept_num = pd.read_csv('concept.csv')
concept = pd.read_csv('stock_concept.csv')
sh = pd.read_csv('sh.csv')
sz = pd.read_csv('sz.csv')
corr = pd.read_csv('corr.csv')
```
### 4.3 填充和去重
```python
stock['行业'] = stock['行业'].fillna('未知')
holder = holder.drop_duplicates(subset=None, keep='first', inplace=False)
```
### 4.4 创建实体
概念、股票、股东、股通
```python
sz = Node('深股通',名字='深股通')
graph.create(sz)
sh = Node('沪股通',名字='沪股通')
graph.create(sh)
for i in concept_num.values:
a = Node('概念',概念代码=i[1],概念名称=i[2])
print('概念代码:'+str(i[1]),'概念名称:'+str(i[2]))
graph.create(a)
for i in stock.values:
a = Node('股票',TS代码=i[1],股票名称=i[3],行业=i[4])
print('TS代码:'+str(i[1]),'股票名称:'+str(i[3]),'行业:'+str(i[4]))
graph.create(a)
for i in holder.values:
a = Node('股东',TS代码=i[0],股东名称=i[1],持股数量=i[2],持股比例=i[3])
print('TS代码:'+str(i[0]),'股东名称:'+str(i[1]),'持股数量:'+str(i[2]))
graph.create(a)
```
### 4.5 创建关系
股票-股东、股票-概念、股票-公告、股票-股通
```python
matcher = NodeMatcher(graph)
for i in holder.values:
a = matcher.match("股票",TS代码=i[0]).first()
b = matcher.match("股东",TS代码=i[0])
for j in b:
r = Relationship(j,'参股',a)
graph.create(r)
print('TS',str(i[0]))
for i in concept.values:
a = matcher.match("股票",TS代码=i[3]).first()
b = matcher.match("概念",概念代码=i[1]).first()
if a == None or b == None:
continue
r = Relationship(a,'概念属于',b)
graph.create(r)
noticesdir = os.listdir("notices\\")
for n in noticesdir:
notice = pd.read_csv("notices\\"+n,encoding="utf_8_sig")
notice['content'] = notice['content'].fillna('空白')
for i in notice.values:
a = matcher.match("股票",TS代码=i[0]).first()
b = Node('公告',日期=i[1],标题=i[2],内容=i[3])
graph.create(b)
r = Relationship(a,'发布公告',b)
graph.create(r)
print(str(i[0]))
for i in sz.values:
a = matcher.match("股票",TS代码=i[0]).first()
b = matcher.match("深股通").first()
r = Relationship(a,'成分股属于',b)
graph.create(r)
print('TS代码:'+str(i[1]),'--深股通')
for i in sh.values:
a = matcher.match("股票",TS代码=i[0]).first()
b = matcher.match("沪股通").first()
r = Relationship(a,'成分股属于',b)
graph.create(r)
print('TS代码:'+str(i[1]),'--沪股通')
# 构建股票间关联
corr = pd.read_csv("corr.csv")
for i in corr.values:
a = matcher.match("股票",TS代码=i[1][:-1]).first()
b = matcher.match("股票",TS代码=i[2][:-1]).first()
r = Relationship(a,str(i[3]),b)
graph.create(r)
print(i)
```
## 5 数据可视化查询
基于Crypher语言，以平安银行为例进行可视化查询。
### 5.1 查看所有关联实体
```cypher
match p=(m)-[]->(n) where m.股票名称="平安银行" or n.股票名称="平安银行" return p;
```
### 5.2 限制显示数量
计算股票间对数收益率的相关系数后，查看与平安银行股票相关联的实体
```cypher
match p=(m)-[]->(n) where m.股票名称="平安银行" or n.股票名称="平安银行" return p limit 300;
```
### 5.3 指定股票间对数收益率相关系数
```cypher
match p=(m)-[]->(n) where m.股票名称="平安银行" and n.股票名称="万科A" return p;
```
## 6 neo4j 图算法
### 6.1.中心度算法(Centralities)
- [PageRank(页面排名)](https://neo4j.com/docs/graph-algorithms/current/algorithms/page-rank/)
- [ArticleRank(文章排名)](https://neo4j.com/docs/graph-algorithms/current/algorithms/article-rank/)
- [Betweenness Centrality (中介中心度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/betweenness-centrality/)
- [Closeness Centrality (接近中心度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/closeness-centrality/)
- [Harmonic Centrality(谐波中心度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/harmonic-centrality/)
### 6.2 社区检测算法(Community detection)
- [Louvain (鲁汶算法)](https://neo4j.com/docs/graph-data-science/current/algorithms/louvain/)
- [Label Propagation (标签传播)](https://neo4j.com/docs/graph-algorithms/current/algorithms/label-propagation/)
- [Connected Components (连通组件)](https://neo4j.com/docs/graph-algorithms/current/algorithms/connected-components/)
- [Strongly Connected Components (强连通组件)]((https://neo4j.com/docs/graph-algorithms/current/algorithms/strongly-connected-components/)
- [Triangle Counting / Clustering Coefficient (三角计数/聚类系数)](https://neo4j.com/docs/graph-algorithms/current/algorithms/triangle-counting-clustering-coefficient/)
### 6.3 路径搜索算法(Path finding)
- [Minimum Weight Spanning Tree (最小权重生成树)](https://neo4j.com/docs/graph-algorithms/current/algorithms/minimum-weight-spanning-tree/)
- [Shortest Path (最短路径)](https://neo4j.com/docs/graph-algorithms/current/algorithms/shortest-path/)
- [Single Source Shortest Path (单源最短路径)](https://neo4j.com/docs/graph-algorithms/current/algorithms/single-source-shortest-path/)
- [All Pairs Shortest Path (全顶点对最短路径)](https://neo4j.com/docs/graph-algorithms/current/algorithms/all-pairs-shortest-path/)
- [A*(A星)](https://neo4j.com/docs/graph-algorithms/current/algorithms/a_star/)
- [Yen’s K-shortest Paths(Yen-K最短路径)](https://neo4j.com/docs/graph-algorithms/current/algorithms/yen-s-k-shortest-path/)
- [Random Walk (随机游走)](https://neo4j.com/docs/graph-algorithms/current/algorithms/random-walk/)
### 6.4 相似性算法(Similarity)
- [Jaccard Similarity (Jaccard相似度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/similarity-jaccard/)
- [Cosine Similarity (余弦相似度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/similarity-cosine/)
- [Pearson Similarity (Pearson相似度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/similarity-pearson/)
- [Euclidean Distance (欧氏距离)](https://neo4j.com/docs/graph-algorithms/current/algorithms/similarity-euclidean/)
- [Overlap Similarity (重叠相似度)](https://neo4j.com/docs/graph-algorithms/current/algorithms/similarity-overlap/)
### 6.5 链接预测(Link Prediction)
- [Adamic Adar(AA)](https://neo4j.com/docs/graph-algorithms/current/algorithms/linkprediction-adamic-adar/)
- [Common Neighbors(共同近邻)](https://neo4j.com/docs/graph-algorithms/current/algorithms/linkprediction-common-neighbors/)
- [Preferential Attachment(优先连接)](https://neo4j.com/docs/graph-algorithms/current/algorithms/linkprediction-preferential-attachment/)
- [Resource Allocation(资源分配)](https://neo4j.com/docs/graph-algorithms/current/algorithms/linkprediction-resource-allocation/)
- [Same Community(共同社区)](https://neo4j.com/docs/graph-algorithms/current/algorithms/linkprediction-same-community/)
- [Total Neighbors(近邻总数)](https://neo4j.com/docs/graph-algorithms/current/algorithms/linkprediction-total-neighbors/)
### 6.6 预处理算法(Preprocessing)
- [One Hot Encoding(独热编码)](https://neo4j.com/docs/graph-algorithms/current/algorithms/one-hot-encoding/)
### 6.7 算法库安装及导入方法
以Windows OS为例，neo4j的算法库并非在安装包中提供，而需要下载算法包：
（1）下载`graph-algorithms-algo-3.5.4.0.jar`
（2）将`graph-algorithms-algo-3.5.4.0.jar`移动至neo4j数据库根目录下的`plugin`中
（3）修改neo4j数据库目录的`conf`中`neo4j.conf`，添加以下配置
```
dbms.security.procedures.unrestricted=algo.*
```
（4）使用以下命令查看所有算法列表
```cypher
CALL algo.list()
```
### 6.8 算法实践——链路预测
#### 6.8.1 Aaamic Adar algorithm
主要基于判断相邻的两个节点之间的亲密程度作为评判标准，2003年由Lada Adamic 和 Eytan Adar在 [Friends and neighbors on the Web](https://www.sciencedirect.com/science/article/abs/pii/S0378873303000091?via%3Dihub) 提出，其中节点亲密度的计算公式如下：
其中`N(u)`表示与节点u相邻的节点集合，若`A(x,y)`表示节点x和节点y不相邻，而该值若越大则紧密度为高。
AAA 算法 cypher 代码参考：
```cypher
MERGE (zhen:Person {name: "Zhen"})
MERGE (praveena:Person {name: "Praveena"})
MERGE (michael:Person {name: "Michael"})
MERGE (arya:Person {name: "Arya"})
MERGE (karin:Person {name: "Karin"})
MERGE (zhen)-[:FRIENDS]-(arya)
MERGE (zhen)-[:FRIENDS]-(praveena)
MERGE (praveena)-[:WORKS_WITH]-(karin)
MERGE (praveena)-[:FRIENDS]-(michael)
MERGE (michael)-[:WORKS_WITH]-(karin)
MERGE (arya)-[:FRIENDS]-(karin)
// 计算 Michael 和 Karin 之间的亲密度
MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
RETURN algo.linkprediction.adamicAdar(p1, p2) AS score
// score: 0.910349
// 基于好友关系计算 Michael 和 Karin 之间的亲密度
MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
RETURN algo.linkprediction.adamicAdar(p1, p2, {relationshipQuery: "FRIENDS"}) AS score
// score: 0.0
```
#### 6.8.2 Common Neighbors
基于节点之间共同近邻数量计算，计算公式如下：
其中N(x)表示与节点x相邻的节点集合，共同近邻表示两个集合的交集，若CN(x,y)值越高，表示节点x和节点y的亲密度越高。
Common Neighbors算法 cypher 代码参考：
```cypher
MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
RETURN algo.linkprediction.commonNeighbors(p1, p2) AS score
```
---
#### 6.8.3 Resource Allocation
资源分配算法，计算公式如下：
其中`N(u)`是与节点`u`相邻的节点集合，RA(x,y)越高表明节点x和节点y的亲密度越大。
Resource Allocation算法 cypher 代码参考：
```cypher
MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
RETURN algo.linkprediction.resourceAllocation(p1, p2) AS score
```
#### 6.8.4 Total Neighbors
指的是相邻节点之间的邻居总数，计算公式如下：
其中`N(u)`是与节点`u`相邻的节点集合。
Total Neighbors算法 cypher 代码参考：
```cypher
MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
RETURN algo.linkprediction.totalNeighbors(p1, p2) AS score
```
官网文档>链路算法>介绍：https://neo4j.com/docs/graph-algorithms/3.5/labs-algorithms/linkprediction/
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⚠️ 备注：部分中文翻译待勘正，请以官网介绍及英文名为准