Learning Bayes Nets for Relational Data and Heterogenous Networks

为了不同的目的，每天都会收集大量的数据。许多（如果不是大多数的话）新数据集包含有关关联实体网络的信息。链接数据的来源包括社交媒体（如Twitter）、社交网络、计算机网络（如Internet）、生物网络以及大多数组织用来维护其数据的关系数据库。一个关键的竞争优势来自于有效地在网络数据中发现信息模式并从中学习以改进不确定情况下的决策的能力。该项目的目的是扩展和增强机器学习和人工智能的计算工具，以便它们可以应用于网络数据。关键的统计问题包括以下内容。(1)不同类型的环节之间是否存在联系？例如，如果用户搜索一个包含某个主题的网页，他们可能会搜索一个包含相同主题的视频吗？(2)哪些个体在网络中特别重要？例如，一些Twitter账户是否比其他账户更能影响他们的关注者？排名网站Klout.com使用来自Twitter、谷歌+、Facebook等7个网络的数据对个人进行排名。（在影响力最高分100分中，贾斯汀·比伯得了92分，巴拉克·奥巴马得了99分）。Klout.com为1亿多人制作分数，每月报告300亿次调用他们的API。(3)我们能否通过寻找个体之间的相似性来解释联系的存在？这些基本问题的答案可以应用于链接预测、评级预测、推荐、市场营销、网络搜索和其他领域，包括主要的商业应用。评级和推荐系统被许多公司用来向客户推荐产品，比如亚马逊和Netflix。

Vast amounts of data are gathered daily for different purposes. Many, if not most, new datasets contain information about networks of linked entities. Sources of link data include social media, such as Twitter, social networks, computer networks such as the Internet, biological networks, and the relational databases that most organizations use to maintain their data. A crucial competitive edge comes with the ability to efficiently find informative patterns in network data and to learn from them to improve decision-making under uncertainty. The aim of this project is to extend and enhance computational tools from Machine Learning and Artificial Intelligence so that they can be applied to network data. Key statistical questions include the following. (1) Are there connections between different types of links? For example, if a user searches for a webpage that covers a topic, are they likely to search for a video that covers the same topic? (2) Which individuals are especially important in a network? For example, do some Twitter accounts influence their followers more than others? The ranking website Klout.com uses data from 7 networks such as Twitter, Google+, Facebook to rank individuals. (Out of a maximum influence score of 100, Justin Bieber scores 92 and Barack Obama 99). Klout.com produces scores for 100+ Million people and reports 30 Billion monthly calls to their API. (3) Can we explain the existence of links by finding similarities among individuals? Answers to these basic questions have applications in predicting links, predicting ratings, making recommendations, marketing, web search, and other areas, including major commercial applications. Rating and recommendation systems are used by many companies to recommend products to customers, such as Amazon and Netflix.