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

• 批准号: 1741634
• 负责人: Yizhou Sun
• 金额: $ 37.65万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741634&HistoricalAwards=false
• 关键词: CAREER; Mining; Exploring; Heterogeneous; Information

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.

在线社交网络，在线论坛和数字政府等异质社交信息网络是数据分析的宝贵来源。 但是，当前的大多数信息网络研究都忽略了所涉及的社会因素，并将其视为图表中的节点和链接。 该项目提供了一种系统的方法，用于分析解决与人类因素有关的问题的这种网络，并认识到不同类型的链接与特定问题的相关性不同。 例如，“导师”链接可能与推荐某人申请特定工作而不是看某个电影更相关。 该项目确定了五个基本研究问题，并为异质社交信息网络中的这些问题提供解决方案：（1）预测缺失的用户和链接特征，（2）识别人格特征，（3）角色检测，（4）社交活动的预测和（5）推荐系统。这些方法共同提供了一种方法，可以将社会理解包括在网络分析中。基本方法是提供概率模型，可以（1）根据域专家的有限标签或启发式方法纳入指导，（2）在复杂的异质信息网络中自动选择最关键的信息，以解决目标问题。例如，对于年龄组预测的用户分析问题，通过定义可能的标签配置的概率来设计一个概率模型，并在不同类型的链接上具有网络结构和优势。 派生的学习算法将仅通过不同类型的链接从几个用户传播标签，每个链接类型的强度将根据该链接类型上标签的配置概率来学习。直觉是，如果“同学”链接类型将两个具有相似年龄的用户带入在一起，则该算法需要将相同的年龄组标签分配给两个是同学的连接用户，并为“同学”链接类型分配了更高的强度权重。该项目将开发基于Spark和GraphX的集成网络挖掘系统，以支持大规模网络上提出的算法。该系统将用作研究工具，用于探索有效的算法质量保证的有效近似值。

项目成果

GPT-GNN: Generative Pre-Training of Graph Neural Networks

• DOI: 10.1145/3394486.3403237
• 发表时间: 2020-01-01
• 期刊: KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
• 作者: Hu, Ziniu;Dong, Yuxiao;Sun, Yizhou
• 通讯作者: Sun, Yizhou

Heterogeneous Graph Transformer

• DOI: 10.1145/3366423.3380027
• 发表时间: 2020-01-01
• 期刊: WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
• 作者: Hu, Ziniu;Dong, Yuxiao;Sun, Yizhou
• 通讯作者: Sun, Yizhou

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

• DOI: 10.1145/3394486.3403275
• 发表时间: 2020-06
• 期刊: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Zhiping Xiao;Weiping Song;Haoyan Xu;Zhicheng Ren;Yizhou Sun

RaRE: Social Rank Regulated Large-scale Network Embedding

• DOI: 10.1145/3178876.3186102
• 发表时间: 2018-04
• 期刊: Proceedings of the 2018 World Wide Web Conference
• 作者: Yupeng Gu;Yizhou Sun;Yanen Li;Yang Yang-Yang

GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases

• DOI: 10.1145/3394486.3403257
• 发表时间: 2020-07
• 期刊: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Zongyue Qin;Yunsheng Bai;Yizhou Sun