HDC
概述
随机游走（Random Walk）从图中的某个节点开始，随机移动至其中一个邻居节点；这个过程通常会重复进行预设的次数。这个概念最早由英国数学家和生物统计学家Karl Pearson于1905年引入，自那时以来，它已经成为了研究各种系统的基石，而不仅限于图论领域。
- K. Pearson, The Problem of the Random Walk (1905)
基本概念
随机游走
随机游走是一种数学模型，用于模拟以随机或不可预测的方式进行的一系列步骤，类似于一个醉汉酒后乱步所形成路径。
基本的随机游走在一维空间中进行：一个节点从数轴的原点开始，每次向上或向下移动一个单位，移动的方向概率相等。一个10步随机游走的示例如下：
以下是多次执行这种随机游走的示例，每次游走100步：
图中的随机游走
在图中，随机游走从一个节点开始，并依次经过相邻节点形成路径。这个过程由游走深度控制，该深度确定要访问的节点数。
嬴图的随机游走算法实现的是经典随机游走。默认情况下，每条边被赋予相同的权重（等于1），从而遍历每条边的概率相等。当指定边权重时，遍历每条边的概率与其权重成正比。需要注意的是，随机游走存在多种变体，例如Node2Vec游走和Struc2Vec游走。
特殊说明
- 节点可以沿自环边游走。
- 随机游走无法从一个孤点开始，因为没有相邻的边可以继续前进。
- 随机游走的结果与边的方向无关。
示例图
在一个空图中运行以下语句定义图结构并插入数据：
ALTER EDGE default ADD PROPERTY {
score float
};
INSERT (A:default {_id: "A"}),
(B:default {_id: "B"}),
(C:default {_id: "C"}),
(D:default {_id: "D"}),
(E:default {_id: "E"}),
(F:default {_id: "F"}),
(G:default {_id: "G"}),
(H:default {_id: "H"}),
(I:default {_id: "I"}),
(J:default {_id: "J"}),
(K:default {_id: "K"}),
(A)-[:default {score: 1}]->(B),
(A)-[:default {score: 3}]->(C),
(C)-[:default {score: 1.5}]->(D),
(D)-[:default {score: 2.4}]->(C),
(D)-[:default {score: 5}]->(F),
(E)-[:default {score: 2.2}]->(C),
(E)-[:default {score: 0.6}]->(F),
(F)-[:default {score: 1.5}]->(G),
(G)-[:default {score: 2}]->(J),
(H)-[:default {score: 2.5}]->(G),
(H)-[:default {score: 1}]->(I),
(I)-[:default {score: 3.1}]->(I),
(J)-[:default {score: 2.6}]->(G);
create().edge_property(@default, "score", float);
insert().into(@default).nodes([{_id:"A"},{_id:"B"},{_id:"C"},{_id:"D"},{_id:"E"},{_id:"F"},{_id:"G"},{_id:"H"},{_id:"I"},{_id:"J"},{_id:"K"}]);
insert().into(@default).edges([{_from:"A", _to:"B", score:1}, {_from:"A", _to:"C", score:3}, {_from:"C", _to:"D", score:1.5}, {_from:"D", _to:"C", score:2.4}, {_from:"D", _to:"F", score:5}, {_from:"E", _to:"C", score:2.2}, {_from:"E", _to:"F", score:0.6}, {_from:"F", _to:"G", score:1.5}, {_from:"G", _to:"J", score:2}, {_from:"H", _to:"G", score:2.5}, {_from:"H", _to:"I", score:1}, {_from:"I", _to:"I", score:3.1}, {_from:"J", _to:"G", score:2.6}]);
创建HDC图
将当前图集全部加载到HDC服务器hdc-server-1上，并命名为 my_hdc_graph：
CREATE HDC GRAPH my_hdc_graph ON "hdc-server-1" OPTIONS {
nodes: {"*": ["*"]},
edges: {"*": ["*"]},
direction: "undirected",
load_id: true,
update: "static"
}
hdc.graph.create("my_hdc_graph", {
nodes: {"*": ["*"]},
edges: {"*": ["*"]},
direction: "undirected",
load_id: true,
update: "static"
}).to("hdc-server-1")
参数
算法名：random_walk
参数名 |
类型 |
规范 |
默认值 |
可选 |
描述 |
|---|---|---|---|---|---|
ids |
[]_id |
/ | / | 是 | 通过_id指定随机游走的起点；若未设置则计算所有点 |
uuids |
[]_uuid |
/ | / | 是 | 通过_uuid指定随机游走的起点；若未设置则计算所有点 |
walk_length |
Integer | ≥1 | 1 |
是 | 每次游走的深度，即访问的节点数量 |
walk_num |
Integer | ≥1 | 1 |
是 | 从每个指定节点开始的游走次数 |
edge_schema_property |
[]"<@schema.?><property>" |
/ | / | 是 | 作为权重的数值类型边属性，权重值为所有指定属性值的总和；不包含指定属性的边将被忽略 |
return_id_uuid |
String | uuid, id, both |
uuid |
是 | 在结果中使用_uuid、_id或同时使用两者来表示点 |
limit |
Integer | ≥-1 | -1 |
是 | 限制返回的结果数；-1返回所有结果 |
文件回写
CALL algo.random_walk.write("my_hdc_graph", {
return_id_uuid: "id",
walk_length: 6,
walk_num: 2
}, {
file: {
filename: "walks"
}
})
algo(random_walk).params({
projection: "my_hdc_graph",
return_id_uuid: "id",
walk_length: 6,
walk_num: 2
}).write({
file:{
filename: 'walks'
}})
结果：
_ids
J,G,H,G,F,D,
D,C,D,C,A,C,
F,G,H,I,I,I,
H,G,H,I,H,G,
B,A,C,E,C,D,
A,C,D,C,D,C,
E,C,E,F,E,C,
C,D,C,E,F,D,
I,I,I,H,G,J,
G,J,G,J,G,H,
J,G,J,G,F,E,
D,C,E,C,D,F,
F,D,C,A,B,A,
H,I,I,I,H,I,
B,A,B,A,C,E,
A,C,D,C,A,B,
E,F,G,F,D,F,
C,E,F,E,F,D,
I,I,H,I,I,I,
G,H,I,I,H,I,
完整返回
CALL algo.random_walk.run("my_hdc_graph", {
return_id_uuid: "id",
walk_length: 6,
walk_num: 2,
edge_schema_property: 'score'
}) YIELD walks
RETURN walks
exec{
algo(random_walk).params({
return_id_uuid: "id",
walk_length: 6,
walk_num: 2,
edge_schema_property: 'score'
}) as walks
return walks
} on my_hdc_graph
结果：
| _ids |
|---|
| ["J","G","J","G","J","G"] |
| ["D","F","E","C","E","C"] |
| ["F","D","F","D","F","G"] |
| ["H","I","I","I","I","H"] |
| ["B","A","C","A","C","D"] |
| ["A","C","A","B","A","B"] |
| ["E","C","E","F","D","C"] |
| ["C","A","C","D","F","D"] |
| ["I","H","I","I","I","I"] |
| ["G","H","G","J","G","J"] |
| ["J","G","J","G","J","G"] |
| ["D","F","D","C","E","C"] |
| ["F","D","C","D","C","E"] |
| ["H","I","H","G","J","G"] |
| ["B","A","C","D","F","G"] |
| ["A","C","D","C","A","C"] |
| ["G","J","G","F","D","F"] |
| ["H","I","I","I","I","H"] |
| ["F","D","F","D","F","G"] |
| ["D","F","E","C","E","C"] |
| ["J","G","J","G","J","G"] |
流式返回
CALL algo.random_walk.stream("my_hdc_graph", {
return_id_uuid: "id",
walk_length: 5,
walk_num: 1,
edge_schema_property: '@default.score'
}) YIELD walks
RETURN walks
exec{
algo(random_walk).params({
return_id_uuid: "id",
walk_length: 5,
walk_num: 1,
edge_schema_property: '@default.score'
}).stream() as walks
return walks
} on hdc_randomWalk
结果：
| _ids |
|---|
| ["J","G","J","G","J"] |
| ["D","F","G","J","G"] |
| ["F","G","F","D","C"] |
| ["H","G","H","G","J"] |
| ["B","A","C","D","F"] |
| ["A","C","A","C","A"] |
| ["E","F","D","F","D"] |
| ["C","D","F","D","F"] |
| ["I","I","I","I","I"] |
| ["G","H","G","J","G"] |