III: Medium: Collaborative Research: Towards Scalable and Interpretable Graph Neural Networks
Description
Graphs are ubiquitous data structures in numerous domains, such as social science (social networks), natural science (physical systems, and protein-protein interaction networks) and knowledge graphs. As new generalizations of traditional deep neural networks to graph structured data, Graph Neural Networks (or GNNs) have demonstrated the power in graph representation learning and have permeated numerous areas of science and technology. However, GNNs also inherited the drawback of traditional deep neural networks, i.e., lacking interpretability. Moreover, the complexity of graph data introduces the scalability as a new limitation for GNNs because graph structured data are not independent. These drawbacks have raised tremendous concerns to adopt GNNs in many critical applications pertaining to fairness, privacy, and safety. Thus, this project aims to tackle the major drawbacks of GNNs and greatly enlarge their usability in critical applications. To achieve the research goal, this project systematically investigates advanced principles for scalable GNNs and new mechanisms to interpret GNNs. The proposed research extends the state-of-the-art GNNs to a new frontier, investigates original problems that entreat innovative solutions and paves the way for a new research endeavor effectively tame graph mining. As many real-world domains problems requires scalable and interpretable graph mining techniques, the project has potential to benefit many real-world applications from various disciplines such as Computer Science, Social Science, Healthcare and Bioinformatics.
This project proposes novel principles and mechanisms for scalable and interpretable graph neural networks to facilitate the adoption of GNNs on critical domains, investigates associated fundamental research issues and develops effective algorithms. The project offers the first comprehensive investigation on these directions, and the designed novel methodologies and tasks will deepen our understanding on the inner working mechanisms of GNNs and contribute to real-world applications. The success of this project will be (1) New scalable and interpretable GNNs with state-of-the-art graph representation learning and predictive performance; (2) Theoretical analysis such as convergence and complexity; and (3) Open-source implementations of all key algorithms and frameworks. Disparate means are planned to disseminate the project and its findings, such as web enabled data and software repositories, books, journal and conference publications, special purpose workshops or tutorials, and industrial collaborations. The project can be effectively integrated to undergraduate and graduate courses as well as in student research projects.
Publications
Conferences
- Enyan Dai, Charu Aggarwal, and Suhang Wang.
``NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs'',
In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21)
- Teng Xiao, Zhengyu Chen, Donglin Wang, and Suhang Wang.
``Learning How to Propagate Messages in Graph Neural Networks'',
In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21)
- Enyan Dai, Yiwei Sun, Kai Shu, and Suhang Wang.
``Labeled Data Generation with Inexact Supervision'',
In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21)
- Yao Ma, Suhang Wang, Lingfei Wu, and Jiliang Tang.
``Attacking Graph Convolutional Networks via Rewiring'',
In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21)
- Tsung-Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, and Vasant Honavar.
``SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series'',
In Proceedings of the Web Conference (WWW-21)
- Enyan Dai, and Suhang Wang.
``Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information'',
In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM-21)
- Tianxiang Zhao, Xiang Zhang, and Suhang Wang.
``GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks'',
In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM-21)
- Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, and Vasant Honavar.
``Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals'',
In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM-21)
- Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang.
``Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks'',
In Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM-20)
- Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, and Suhang Wang.
``Semi-Supervised Graph-to-Graph Translations'',
In Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM-20)
Resources
Code
Project Members
Acknowledgments
This project is suported by National Science Foundation (NSF) under Grant #1955851.
Any opinions, findings, and conclusions or recommendations expressed here are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Created by Suhang Wang who can be reached
at szw494 at psu.edu.
Webmaster: Enyan Dai, Email: emd5759 at psu.edu.
Last Upadted: June 14, 2021