CAREER: Mining and Exploring Heterogeneous Information Networks with Social Factors

职业：挖掘和探索具有社会因素的异构信息网络

基本信息

批准号：
1453800
负责人：
Yizhou Sun
金额：
$ 50.2万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-05-01 至 2017-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453800&HistoricalAwards=false
关键词：
CAREER Mining Exploring Heterogeneous Information

项目摘要

Heterogeneous social information networks, such as online social networks, online forums, and digital government, are valuable sources for data analysis. However, most of the current information network studies ignore the social factors involved and treat people and their interactions simply as nodes and links in graphs. This project provides a systematic approach for analyzing such networks that addresses human factor-related questions, recognizing that different types of links have different relevance to a particular question. For example, a "mentor" link might be much more relevant to recommending someone to apply for a particular job rather than see a certain movie. This project identifies five fundamental research problems and provides solutions to these problems in heterogeneous social information networks: (1) predicting missing user and link characteristics, (2) identifying personality traits, (3) role detection, (4) prediction of social activities, and (5) recommender systems. Together these provide a way to include social understanding in analysis of networks.The basic approach is to provide probabilistic models that can (1) incorporate guidance in terms of either limited labels or heuristics from domain experts, and (2) automatically select the most critical information in complicated heterogeneous information networks for the target problem. For example, for the user profiling problem of age group prediction, a probabilistic model is designed via defining the probability of a possible label configuration given the network structure and strengths on different types of links. The derived learning algorithm will propagate the labels from only a few users via different types of links, and the strength of each link type will be learned according to the configuration probability of labels on that link type. The intuition is that if the "classmates" link type brings two users with similar age together, the algorithm needs to assign the same age group label to the two connected users that are classmates and assigns a higher strength weight to the "classmates" link type. The project will develop an integrated network mining system based on Spark and GraphX, to support the proposed algorithms on large-scale networks. This system will be used as a research vehicle for exploring efficient approximations with quality guarantees for the proposed algorithms.

异构社会信息网络，例如在线社交网络、在线论坛和数字政府，是数据分析的宝贵来源。然而，当前大多数信息网络研究忽略了所涉及的社会因素，并将人及其交互简单地视为图中的节点和链接。该项目提供了一种系统方法来分析此类网络，解决与人为因素相关的问题，认识到不同类型的链接与特定问题具有不同的相关性。例如，“导师”链接可能与推荐某人申请特定工作而不是观看特定电影更相关。该项目确定了五个基本研究问题，并为异构社交信息网络中的这些问题提供了解决方案：（1）预测缺失的用户和链接特征，（2）识别个性特征，（3）角色检测，（4）社交活动预测， (5)推荐系统。这些共同提供了一种将社会理解纳入网络分析的方法。基本方法是提供概率模型，该模型可以（1）结合有限标签或领域专家的启发式指导，以及（2）自动选择最关键的针对目标问题的复杂异构信息网络中的信息。例如，对于年龄组预测的用户画像问题，在给定网络结构和不同类型链接的强度的情况下，通过定义可能的标签配置的概率来设计概率模型。派生的学习算法将通过不同类型的链接传播来自少数用户的标签，并且将根据该链接类型上标签的配置概率来学习每种链接类型的强度。直觉是，如果“同学”链接类型将两个年龄相似的用户聚集在一起，则算法需要为这两个作为同学的连接用户分配相同的年龄组标签，并为“同学”链接类型分配更高的强度权重。该项目将开发一个基于Spark和GraphX的集成网络挖掘系统，以支持大规模网络上所提出的算法。该系统将用作研究工具，用于探索有效的近似方法，并保证所提出算法的质量。