III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation

III：媒介：协作研究：具有特定应用适应能力的可扩展异构网络嵌入框架

• 批准号: 1763325
• 负责人: Philip Yu
• 金额: $ 65万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763325&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Extensible

Network data is ubiquitous in the real-world, and many online websites providing various kinds services can all be represented as networks, e.g., online social networks, e-commerce networks, and academic networks. Learning and mining of network structured data have been one of the most popular yet challenging research problems studied in recent years. This project will study the problem of how to find a simple, yet effective representation for each network node, which can capture its characteristics or role in the network based on its connections. This is referred to as the network embedding problem. As an effective tool to transform network data into classic feature-vector representations, network embedding aims at mapping the network data into a low-dimensional feature space, i.e., with a small number of features for each network node. With the embedding results, all these aforementioned networks will be benefited to improve their services provided for the public. This project focuses on developing a general network embedding framework, and investigating its extension to application-oriented, multi-network and dynamic-network scenarios. This project will help support female and minority students to participate in academic research about network embedding. Network embedding studied in this project is a challenging learning task due to many reasons. (1) Data perspective, the heterogeneity of real-world social network data renders existing homogeneous-network oriented embedding models failing to work; (2) Structure preserving perspective, many first-order proximity based embedding methods can hardly preserve the complex social network structure with heterogeneous node types; and (3) Task perspective, the detachment of embedding process with external tasks makes the learnt results ineffective for application tasks with specific objectives. This project aims at tackling these challenges by proposing a novel extensible heterogeneous social network embedding model, which can effectively incorporate the objectives of external tasks in the learning process. This project covers five main themes: (1) extensible heterogeneous network embedding foundation; (2) application oriented embedding of single heterogeneous network; (3) embedding over multiple heterogeneous network for network alignment; (4) dynamic heterogeneous network embedding for friend recommendation; and (5) advanced scalable heterogeneous network embedding technique exploration. This project will greatly enrich the fundamental principles and technologies of social network mining and data mining. In terms of the broader impact, advances in network embedding analysis have transformative potential for fundamental advances in understanding the behavior and activities of the social networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络数据在现实世界中无处不在，许多提供各种服务的在线网站都可以表示为网络，在线社交网络、电子商务网络和学术网络。网络结构化数据的学习和挖掘是近年来研究的热点和挑战性问题之一。本项目将研究如何为每个网络节点找到一个简单而有效的表示方法，该方法可以根据其连接来捕获其在网络中的特性或角色。这被称为网络嵌入问题。作为一种将网络数据转换为经典特征向量表示的有效工具，网络嵌入旨在将网络数据映射到低维特征空间，即，每个网络节点具有少量特征。嵌入的结果，所有这些上述网络将有利于改善他们为公众提供的服务。本项目的重点是开发一个通用的网络嵌入框架，并研究其扩展到面向应用的，多网络和动态网络的场景。该项目将帮助支持女性和少数民族学生参与有关网络嵌入的学术研究。由于多种原因，本项目研究的网络嵌入是一项具有挑战性的学习任务。(1)（2）结构保持方面，许多基于一阶邻近度的嵌入方法难以保持节点类型异构的复杂社会网络结构;（3）任务视角，嵌入过程与外部任务的分离使得学习结果对特定目标的应用任务无效。该项目旨在通过提出一种新的可扩展的异构社会网络嵌入模型来应对这些挑战，该模型可以有效地将外部任务的目标纳入学习过程。该项目包括五个主题：（1）可扩展的异构网络嵌入基础;（2）面向应用的单一异构网络嵌入;（3）基于网络对齐的多异构网络嵌入;（4）基于好友推荐的动态异构网络嵌入;（5）高级可扩展异构网络嵌入技术探索。该项目将极大地丰富社会网络挖掘和数据挖掘的基本原理和技术。就更广泛的影响而言，网络嵌入分析的进步对于理解社交网络行为和活动的根本性进步具有变革性潜力。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

• DOI: 10.48550/arxiv.2206.01535
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Yizhen Zheng;Shirui Pan;Vincent C. S. Lee;Yu Zheng;Philip S. Yu

Graph Self-Supervised Learning: A Survey

• DOI: 10.1109/tkde.2022.3172903
• 发表时间: 2023-06-01
• 期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
• 影响因子: 8.9
• 作者: Liu, Yixin;Jin, Ming;Yu, Philip S.
• 通讯作者: Yu, Philip S.

Network Embedding With Completely-Imbalanced Labels

具有完全不平衡标签的网络嵌入

• DOI: 10.1109/tkde.2020.2971490
• 发表时间: 2021
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Zheng Wang;Ye Xiaojun;Wang Chaokun;Cui Jian;Yu Philip S.
• 通讯作者: Yu Philip S.

A Self-supervised Mixed-curvature Graph Neural Network

• DOI: 10.1609/aaai.v36i4.20333
• 发表时间: 2021-12
• 期刊: ArXiv
• 作者: Li Sun;Zhongbao Zhang;Junda Ye;Hao Peng;Jiawei Zhang;Sen Su;Philip S. Yu

A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning

• DOI: 10.1145/3511808.3557222
• 发表时间: 2022-08
• 期刊: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
• 作者: Li Sun;Junda Ye;Hao Peng;Philip S. Yu