neo4j实现PageRank算法 该内容源于neo4j算法数据库GDS操作手册1.4版，只是记录下来方便自己理解，反思回顾。

例子：创建一个属性图

CREATE
  (home:Page {name:'Home'}),
  (about:Page {name:'About'}),
  (product:Page {name:'Product'}),
  (links:Page {name:'Links'}),
  (a:Page {name:'Site A'}),
  (b:Page {name:'Site B'}),
  (c:Page {name:'Site C'}),
  (d:Page {name:'Site D'}),

  (home)-[:LINKS {weight: 0.2}]->(about),
  (home)-[:LINKS {weight: 0.2}]->(links),
  (home)-[:LINKS {weight: 0.6}]->(product),
  (about)-[:LINKS {weight: 1.0}]->(home),
  (product)-[:LINKS {weight: 1.0}]->(home),
  (a)-[:LINKS {weight: 1.0}]->(home),
  (b)-[:LINKS {weight: 1.0}]->(home),
  (c)-[:LINKS {weight: 1.0}]->(home),
  (d)-[:LINKS {weight: 1.0}]->(home),
  (links)-[:LINKS {weight: 0.8}]->(home),
  (links)-[:LINKS {weight: 0.05}]->(a),
  (links)-[:LINKS {weight: 0.05}]->(b),
  (links)-[:LINKS {weight: 0.05}]->(c),
  (links)-[:LINKS {weight: 0.05}]->(d);

1：属性图如下 在这里插入图片描述 2：给这个属性图起一个名字

CALL gds.graph.create(
  'myGraph',
  'Page',
  'LINKS',
  {
    relationshipProperties: 'weight'
  }
)

3：可以评估一下这个属性图所占内存

CALL gds.pageRank.write.estimate('myGraph', {
  writeProperty: 'pageRank',
  maxIterations: 20,
  dampingFactor: 0.85
})
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory

4：现在可以调用GDS中的PageRank算法

CALL gds.pageRank.stream('myGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC, name ASC

5：还有stats、mutate和write执行模式，此处不在赘述 6：下面执行带权重的算法计算

CALL gds.pageRank.stream('myGraph', {
  maxIterations: 20,
  dampingFactor: 0.85,
  relationshipWeightProperty: 'weight'
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC, name ASC

7：这里约束条件tolerance设为0.1，低于此值，算法结束，结果返回

CALL gds.pageRank.stream('myGraph', {
  maxIterations: 20,
  dampingFactor: 0.85,
  tolerance: 0.1
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC, name ASC

8：阻尼系数dampingFactor：也就是概率值改变会引起中心度值不同，这里变为0.05，看起来和默认的0.85差不多，源于图节点比较少，还不足以引起质变

CALL gds.pageRank.stream('myGraph', {
  maxIterations: 20,
  dampingFactor: 0.05
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC, name ASC

9：个性化PageRank算法，其实就是制定一个特定的起点，重点关注这个指定点的中心度

MATCH (siteA:Page {name: 'Site A'})
CALL gds.pageRank.stream('myGraph', {
  maxIterations: 20,
  dampingFactor: 0.85,
  sourceNodes: [siteA]
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC, name ASC

相关链接 这篇文章关于PageRank算法的通俗理解 (https://segmentfault.com/a/1190000015409175)