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图分析,图计算或者图推理的方法 #267

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mengquanrun opened this issue Dec 10, 2018 · 3 comments

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@mengquanrun
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@mengquanrun mengquanrun commented Dec 10, 2018

Expected behavior 期望表现

{type something here...}
@javeme 想请教百度在图分析和图推理方向上会采用什么方法呢?在项目介绍中说应用场景在反欺诈,打击黑产等方向,请问贵团队在实际应用场景中会如何利用hugegraph这个工具呢?
常用的主要就是您们提供的那些最短路径等api么?

Actual behavior 实际表现

{type something here...}

Steps to reproduce the problem 复现步骤

  1. {step 1}
  2. {step 2}
  3. {step 3}

Status of loaded data 数据状态

Vertex/Edge summary 数据量

  • loaded vertices amount: {like 10 million}
  • loaded edges amount: {like 20 million}
  • loaded time: {like 200s}

Vertex/Edge example 数据示例

{type something here...}

Schema(VertexLabel, EdgeLabel, IndexLabel) 元数据结构

{type something here...}

Specifications of environment 环境信息

  • hugegraph version: {like v0.7.4}
  • operating system: {like centos 7.4, 32 CPUs, 64G RAM}
  • hugegraph backend: {like cassandra 3.10, cluster with 20 nodes, 3 x 1TB HDD disk each node}
@fisherinbox

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@fisherinbox fisherinbox commented Dec 10, 2018

同问

@javeme

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@javeme javeme commented Dec 10, 2018

@mengquanrun @fisherinbox 反欺诈这个业务场景大体涉及如下典型方法:

  1. 关系特征检测
    • 高聚合度关系检测(比如大量用户使用同一个设备)
    • 自相矛盾关系检测
    • 关系环路检测(比如循环担保)
      比如环路检测可使用Rings API
  2. 关联度检测
    关联度检测的典型技术包括:
    • 检测多层关系网络中是否包含高风险节点
    • 通过PersonalRank、PageRank等算法计算关系网络中节点的风险评分
      比如3度关联是否触黑,可使用K-neighbor API
  3. 欺诈团伙检测
    通过社区发现等算法来检测团伙,然后根据团伙中各成员的风险评分综合计算整个团伙的风险程度,从而识别出高风险的欺诈团伙。我们主要使用2个社区发现算法:简单高效的标签传播算法LPA,以及基于模块度优化迭代算法Louvain,Louvain的优点是可以发现多层次性的社区。
@mengquanrun

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@mengquanrun mengquanrun commented Dec 12, 2018

感谢,我学习下~ @javeme

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