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.
|Effective start/end date||7/1/22 → 6/30/24|
- National Science Foundation: $175,000.00