文章目录
创建一个社交网络属性图（这是一个有向图）
CREATE (alice:User {name: 'Alice'}),
(bridget:User {name: 'Bridget'}),
(charles:User {name: 'Charles'}),
(doug:User {name: 'Doug'}),
(mark:User {name: 'Mark'}),
(michael:User {name: 'Michael'}),
(alice)-[:FOLLOWS]->(doug),
(alice)-[:FOLLOWS]->(bridget),
(alice)-[:FOLLOWS]->(charles),
(mark)-[:FOLLOWS]->(doug),
(mark)-[:FOLLOWS]->(michael),
(bridget)-[:FOLLOWS]->(doug),
(charles)-[:FOLLOWS]->(doug),
(michael)-[:FOLLOWS]->(doug)
一、属性图如下
二、实现算法(unweight)
1.stream模式计算每个点的入度
CALL gds.alpha.degree.stream({
nodeProjection: 'User',
relationshipProjection: {
FOLLOWS: {
type: 'FOLLOWS',
orientation: 'REVERSE'
}
}
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS followers
ORDER BY followers DESC
这句话orientation: ‘REVERSE’；表示节点关系的计算方向逆转，该算法默认计算出度，现在逆转计算入度
2.各节点入度计算结果如下
3.stream模式计算每个点的出度
CALL gds.alpha.degree.stream({
nodeProjection: 'User',
relationshipProjection: 'FOLLOWS'
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS followers
ORDER BY followers DESC
4.各节点出度计算结果如下
4.结论
We can see that Doug is the most popular user in our imaginary Twitter graph, with 5 followers - all other users follow him, but he doesn’t follow anybody back. In the real Twitter network celebrities have very high follower counts but tend to follow very few back people. We could therefore consider Doug a celebrity!
我们可以得出Doug是个名人，因为关注他的多，他关注的少，所以他是一个广受欢迎的“点”，可以被推荐给他人关注或者需要重点关注他。
三、实现算法(weight)
1.带权重的属性图如下
2.stream模式计算每个点的入度
CALL gds.alpha.degree.stream({
nodeProjection: 'User',
relationshipProjection: {
FOLLOWS: {
type: 'FOLLOWS',
orientation: 'REVERSE',
properties: 'score'
}
},
relationshipWeightProperty: 'score'
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS weightedFollowers
ORDER BY weightedFollowers DESC
3.各节点入度计算结果如下
4.各节点出度计算结果如下
CALL gds.alpha.degree.stream({
nodeProjection: 'User',
relationshipProjection: {
FOLLOWS: {
type: 'FOLLOWS',
properties: 'score'
}
},
relationshipWeightProperty: 'score'
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS weightedFollowers
ORDER BY weightedFollowers DESC
5.各节点出度计算结果如下
Doug still remains our most popular user, but there isn’t such a big gap to the next person. Charles and Michael both only have one follower, but those relationships have a high relationship weight.
增加权重之后，我们发现Charies和Michael的分数并不比Doug逊色，说明在真实现实世界，考虑权重影响因素还是很有必要的。