III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery

III：小：异构网络融合以实现协同知识发现

• 批准号: 1526499
• 负责人: Philip Yu
• 金额: $ 50万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526499&HistoricalAwards=false
• 关键词: III; Small; Fusion; Heterogeneous; Networks

Online social networks, such as Facebook, Twitter and Foursquare, have become increasingly popular in recent years. These online social networks contain abundant information about the users and their activities. Nowadays, to enjoy more social network services, people are getting involved in multiple social networks simultaneously. However, the accounts of the same user in different social networks are mostly isolated without any connection or correspondence to each other. This project has the potential to make fundamental, disruptive advances in fusion of heterogeneous networks for synergistic knowledge discovery. The success of this project will dramatically extend and change the current social network studies in data mining. In addition to social network analysis, this work can also be beneficial to scientific research such as life sciences on biological networks. The analytic tools developed and data collected will be made available to the public for free download. The team will investigate the principles, methodologies and algorithms for the synergistic knowledge discovery across multiple partially aligned social networks, and evaluate the corresponding benefits. They plan to address the challenge on effective transfer of relevant knowledge across partially aligned networks, which will depend upon not only the relatedness of the different networks, but also the target application, e.g., link prediction vs clustering vs information diffusion. A general methodology will be developed, which will be shown to work for a diverse set of applications, while the specific parameter settings can be learned for each application using some training data. The problems studied include (1) Partial Network Alignment, (2) Integrated Anchor and Social Link Prediction, (3) Mutual Clustering, and (4) Cross-Networks Influence Maximization. This proposal will address these four major research problems systematically based on a unified concept: integrated anchor and social meta paths (including both intra- network and inter-network meta paths) for relationship exploration.

近年来，在线社交网络（例如Facebook，Twitter和Foursquare）变得越来越受欢迎。这些在线社交网络包含有关用户及其活动的丰富信息。如今，为了享受更多的社交网络服务，人们同时参与了多个社交网络。但是，不同社交网络中同一用户的帐户大部分是隔离的，而没有任何连接或对应关系。该项目有可能在融合异质网络的协同知识发现方面取得根本，破坏性的进步。该项目的成功将大大扩展并改变数据挖掘中当前的社交网络研究。除了社交网络分析外，这项工作还可以对科学研究（例如生物网络上的生命科学）有益。开发的分析工具和收集的数据将免费下载。 该团队将调查多个部分一致的社交网络中协同知识发现的原理，方法和算法，并评估相应的好处。他们计划应对跨部分对齐网络有效传输相关知识的挑战，这不仅取决于不同网络的相关性，还取决于目标应用程序，例如链接预测与群集与信息扩散。将开发一种一般方法，该方法将显示为各种应用程序集，而可以使用一些培训数据为每个应用程序学习特定的参数设置。研究的问题包括（1）部分网络对齐，（2）集成的锚和社会链接预测，（3）相互聚类和（4）跨网络影响最大化。该建议将根据统一的概念系统地解决这四个主要的研究问题：集成的锚和社会元路径（包括网络内网络和网络间的元元路径）以进行关系探索。

项目成果

Multi-View Fusion with Extreme Learning Machine for Clustering

• DOI: 10.1145/3340268
• 发表时间: 2019-10
• 期刊: ACM Transactions on Intelligent Systems and Technology (TIST)
• 作者: Yongshan Zhang;Jia Wu;Chuan Zhou;Z. Cai;Jian Yang-;Philip S. Yu

Efficient Traffic Estimation With Multi-Sourced Data by Parallel Coupled Hidden Markov Model

基于并行耦合隐马尔可夫模型的多源数据高效流量估计

• DOI: 10.1109/tits.2018.2870948
• 发表时间: 2019-08
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Senzhang Wang;Xiaoming Zhang;Fenxiang Li;Philip S. Yu;Zhiqiu Huang
• 通讯作者: Zhiqiu Huang

Deep Heterogeneous Social Network Alignment

• DOI: 10.1109/cogmi48466.2019.00015
• 发表时间: 2019-12
• 期刊: 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)
• 作者: Lin Meng;Yuxiang Ren;Jiawei Zhang;Fanghua Ye;Philip S. Yu

JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

• DOI: 10.1109/bigdata47090.2019.9006266
• 发表时间: 2019-10
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Zhiwei Liu;Lei Zheng;Jiawei Zhang;Jiayu Han;Philip S. Yu

DNA: Dynamic Social Network Alignment

• DOI: 10.1109/bigdata47090.2019.9006430
• 发表时间: 2019-10
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Li Sun;Zhongbao Zhang;Pengxin Ji;Jian Wen;Sen Su;Philip S. Yu