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PUT AI TO WORK
June 18-21, 2019
Beijing, CN

非监督学习在大规模图谱上的案例应用和开源算法剖析

此演讲使用中文 (This will be presented in Chinese)

Mingxi Wu (TigerGraph)
14:5015:30 Thursday, June 20, 2019
模型与方法 (Models and Methods)
Location: 多功能厅6A+B (Function Room 6A+B)

必要预备知识 (Prerequisite Knowledge)

  • Familiarity with SQL

您将学到什么 (What you'll learn)

  • 非监督学习图算法有着广泛的应用
  • GSQL开源图算法库可以被扩展和定制,大大降低非监督学习的门槛。

描述 (Description)

图数据上的非监督学习在激活大数据的经济价值上有着广泛和不可替代的作用。
PageRank能够发掘重要的实体, 社区发掘(community detection)可以找到具有某种特性的群体,紧密度中心性算法(closeness centrality)可以自动找到远离群体的个体。所有这些算法都是非监督的学习。 我们分享一些具体客户案例来展示他们的价值,同时分享怎样在大数据上灵活应用这些开源算法。

-社区发掘案例:针对某一个特定(地区,病历)病人群体,怎样找对他们最有影响的供药机构?哪些病人被挖掘出来的供药机构影响?这些病人群体的规模和社会影响有多大?

-其他社区发掘案例:1)挖掘股票交易群体和共同交易特征。2)挖掘具有某种供需特征的特殊群体,更好调度邮寄服务。 3)挖掘犯罪团伙和集体欺诈的团体。

-PageRank: 怎样定制PageRank来找到最有影响力的实体。

通过这些案例分析,我们展示并深度剖析一组开源的非监督学习发掘图算法在客户中的应用,并示例如何轻松定制和扩展这些算法。

Photo of Mingxi Wu

Mingxi Wu

TigerGraph

Mingxi Wu is the vice president of engineering at TigerGraph, a Silicon Valley-based startup building a world-leading real-time graph database. Over his career, Mingxi has focused on database research and data management software. Previously, he worked in Microsoft’s SQL Server Group, Oracle’s Relational Database Optimizer Group, and Turn Inc.‘s Big Data Management Group. Lately, his interest has turned to building an easy-to-use and highly expressive graph query language. He’s won research awards from the most prestigious publication venues in database and data mining, including SIGMOD, KDD, and VLDB, and has authored five US patents with three more international patents pending. Mingxi holds a PhD specializing in both database and data mining from the University of Florida.

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