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，Facebook、Twitter和Foursquare等在线社交网络越来越受欢迎。这些在线社交网络包含了大量关于用户及其活动的信息。如今，为了享受更多的社交网络服务，人们同时参与到多个社交网络中。然而，同一用户在不同社交网络上的账户大多是相互隔离的，彼此之间没有任何联系或对应。该项目有可能在融合异构网络以实现协同知识发现方面取得根本性的突破性进展。该项目的成功将极大地扩展和改变当前数据挖掘领域的社会网络研究。除了社会网络分析，这项工作也可以有利于科学研究，如生物网络上的生命科学。开发的分析工具和收集的数据将免费提供给公众下载。该团队将研究跨多个部分对齐的社交网络进行协同知识发现的原则、方法和算法，并评估相应的好处。他们计划解决相关知识在部分对齐网络之间有效转移的挑战，这不仅取决于不同网络的相关性，还取决于目标应用，例如，链接预测、聚类和信息扩散。将开发一种通用的方法，该方法将被证明适用于各种不同的应用程序，同时可以使用一些训练数据为每个应用程序学习具体的参数设置。研究的问题包括(1)部分网络对齐，(2)整合锚点和社会链接预测，(3)相互聚类，(4)跨网络影响最大化。本提案将基于一个统一的概念系统地解决这四个主要的研究问题：用于关系探索的整合锚和社会元路径（包括网络内和网络间元路径）。

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