CRII:III:Towards Advanced Filtering and Pooling Operations for Graph Neural Networks

CRII:III：走向图神经网络的高级过滤和池化操作

• 批准号: 2153326
• 负责人: Yao Ma
• 金额: $ 17.5万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153326&HistoricalAwards=false
• 关键词: CRII; III; Towards; Advanced; Filtering

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).In recent years, we have witnessed a rapid growth in our ability to generate and gather data from numerous platforms in the online world and various sensors in the physical world. Graphs provide a universal representation for a variety of data including online social networks, knowledge graphs, transportation networks, and chemical compounds. Entities can usually be represented as nodes while their relations can be denoted represented as edges. Many important real-world applications on these data can be treated as computational tasks on graphs. A crucial step to facilitate these tasks is to learn good vector representations either for nodes or graphs. Recently, graph neural networks, which generalize deep learning techniques to graphs, have been widely adopted to learning representations for graphs. Though graph neural networks have advanced numerous real-world applications from various fields, they still suffer from many limitations in terms of efficacy and efficiency. This project aims to address these limitations by conducting theoretical analysis and developing innovative algorithms. This project is specifically motivated by applications to computational social science, computational biology, and fraud detection in e-commerce. Furthermore, this project will involve graduate and undergraduate students in pursuing their theses or honor projects. Discoveries and research findings of this project will be tightly integrated into several current and new courses at the New Jersey Institute of Technology.The technical aims of the project are divided into two tasks corresponding to the two major building components of graph neural networks: graph filtering operations and graph pooling operations. The graph filtering operation aims to refine node representations for all nodes in a graph. On the other hand, the graph pooling operation aims to summarize node representations to obtain a graph representation. The first task aims to investigate graph filtering operations under heterophily—a setting typically poses great challenges for graph filtering operations. In particular, the investigator will conduct theoretical analyses on graph filtering operations to gain deeper insights into their intrinsic mechanism, especially under the scenario of heterophily. Then, based on these understandings, more advanced graph neural networks models will be proposed to handle heterophilous graphs. The second task aims to develop more efficient and effective graph pooling operations. The investigators will explore and develop graph pooling operations based on clustering and down-sampling process. To improve the efficacy and efficiency of the graph pooling operations, the clustering/down-sampling process will be nicely incorporated into the entire learning framework in an end-to-end way.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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助（公法117-2）。近年来，我们目睹了我们从在线世界中的许多平台和物理世界中的各种传感器中生成和收集数据的能力的迅速增长。图为各种数据提供了通用表示形式，包括在线社交网络，知识图，运输网络和化合物。通常可以将实体表示为节点，而它们的关系可以表示为边缘。这些数据上的许多重要的现实应用程序可以视为图表上的计算任务。促进这些任务的关键步骤是学习节点或图形的良好矢量表示。最近，将深度学习技术推广到图形的图形神经网络已被广泛用于图表的学习表示。尽管图神经网络已从各个领域提出了许多现实世界的应用，但在效率和效率方面，它们仍然受到许多限制。该项目旨在通过进行理论分析和开发创新算法来解决这些局限性。该项目是由在电子商务中的计算社会科学，计算生物学和欺诈检测中的应用专门激励的。此外，该项目将涉及毕业生和本科生从事他们​​的论文或荣誉项目。该项目的发现和研究发现将紧密整合到新泽西技术学院的几个当前和新课程中。该项目的技术目的分为与图形神经网络的两个主要建筑物相对应的两个任务：图形过滤操作和图形池操作。图表过滤操作旨在完善图中所有节点的节点表示。另一方面，图形合并操作旨在汇总节点表示形式以获得图表表示。第一个任务旨在调查异性疾病下的图形过滤操作 - 这种设置通常对图形过滤操作构成巨大挑战。特别是，研究者将对图形过滤操作进行理论分析，以更深入地了解其内在机制，尤其是在异性恋的情况下。然后，基于这些理解，将提出更先进的图形中性网络模型来处理异性图。第二个任务旨在开发更有效的图形合并操作。研究人员将根据聚类和下采样过程探索和开发图形池操作。为了提高图形合并操作的效率和效率，将以端到端方式将聚类/下采样过程很好地纳入整个学习框架中。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

项目成果

Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User Profiles

• DOI: 10.1109/tkde.2023.3272652
• 发表时间: 2023-12
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Wenqi Fan;Xiangyu Zhao;Qing Li;Tyler Derr;Yao Ma;Hui Liu;Jianping Wang;Jiliang Tang

Graph Enhanced BERT for Query Understanding

• DOI: 10.1145/3539618.3591845
• 发表时间: 2022-04
• 期刊: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Juanhui Li;Yao Ma;Weizhen Zeng;Suqi Cheng;Jiliang Tang;Shuaiqiang Wang;Dawei Yin

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?

• DOI: 10.18653/v1/2023.acl-long.597
• 发表时间: 2022-05
• 作者: Juanhui Li;Harry Shomer;Jiayu Ding;Yiqi Wang;Yao Ma;Neil Shah;Jiliang Tang;Dawei Yin