business—social, intent, consumption, interest, and mobile—and says that the ability
to leverage these graphs provides a “sustainable competitive advantage.”
For example, Twitter’s data is easily represented as a graph. In Figure 1-1 we see a small
network of followers. The relationships are key here in establishing the semantic context:
namely, that Billy follows Harry, and that Harry, in turn, follows Billy. Ruth and Harry
likewise follow each other, but sadly, although Ruth follows Billy, Billy hasn’t (yet)
reciprocated.
Figure 1-1. A small social graph
Of course, Twitter’s real graph is hundreds of millions of times larger than the example
in Figure 1-1, but it works on precisely the same principles. In Figure 1-2 we’ve expanded
the graph to include the messages published by Ruth.
2 | Chapter 1: Introduction
剩余222页未读，继续阅读
biussx
- 粉丝: 0
- 资源: 1
我的内容管理
展开
最新资源
- 计算机人脸表情动画技术发展综述
- 关系数据库的关键字搜索技术综述：模型、架构与未来趋势
- 迭代自适应逆滤波在语音情感识别中的应用
- 概念知识树在旅游领域智能分析中的应用
- 构建is-a层次与OWL本体集成：理论与算法
- 基于语义元的相似度计算方法研究：改进与有效性验证
- 网格梯度多密度聚类算法：去噪与高效聚类
- 网格服务工作流动态调度算法PGSWA研究
- 突发事件连锁反应网络模型与应急预警分析
- BA网络上的病毒营销与网站推广仿真研究
- 离散HSMM故障预测模型：有效提升系统状态预测
- 煤矿安全评价：信息融合与可拓理论的应用
- 多维度Petri网工作流模型MD_WFN：统一建模与应用研究
- 面向过程追踪的知识安全描述方法
- 基于收益的软件过程资源调度优化策略
- 多核环境下基于数据流Java的Web服务器优化实现提升性能
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
