CAREER: Mining and Exploring Heterogeneous Information Networks with Social Factors

异构的社会信息网络，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，目前的信息网络研究大多忽略了所涉及的社会因素，并把人们和他们的互动简单地作为节点和链接图。 该项目提供了一个系统的方法来分析这种网络，解决人为因素相关的问题，认识到不同类型的链接有不同的相关性，一个特定的问题。 例如，“导师”链接可能更适合推荐某人申请特定的工作，而不是看某部电影。 该项目确定了五个基本研究问题，并提供了解决这些问题的方法，在异构的社会信息网络：（1）预测缺失的用户和链接特征，（2）识别人格特质，（3）角色检测，（4）预测的社会活动，和（5）推荐系统。基本的方法是提供概率模型，这些模型可以（1）结合领域专家的有限标签或分类指导，（2）自动选择目标问题的复杂异构信息网络中最关键的信息。例如，对于年龄组预测的用户分析问题，通过定义给定网络结构和不同类型链路上的强度的可能标签配置的概率来设计概率模型。 推导出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，并且根据标签在该链接类型上的配置概率来学习每个链接类型的强度。直觉是，如果“同学”链接类型将两个年龄相似的用户带到一起，则算法需要为作为同学的两个连接的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的算法。该系统将被用来作为一个研究工具，探索有效的近似与质量保证所提出的算法。

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.