In many kinds of networks, it is interesting to try to predict which links exist or could exist even though they have not been observed so far. For example, in order to make recommendations, Netflix predicts new links between users and movies - movies that the user has not watched yet but is likely to enjoy based on the ratings that similar users have given to similar movies. Link prediction has been well studied for movie and product recommendation, but link prediction between species in an ecosystem has received less attention. Understanding and predicting unobserved links in ecological networks is important for natural resource management, species monitoring, and crop production. Link prediction in ecological networks has some similarities to link prediction for recommendation but also some sharp contrasts. In particular, ecological network data contain more errors than user-movie or user-product networks because field sampling cannot capture all of the true links in the network. Ecological networks are also influenced by factors like the abundance of species and competition for limited resources, and these networks change over time and space. Led by a cross-disciplinary team of investigators, this project will thoroughly address the unique challenges that arise in ecological networks. It will put forth a unified framework including modeling tools and computational infrastructure for analyzing ecological networks from incomplete data over time and space.

In terms of theory and methods, many key aspects of link prediction in ecological networks such as statistical models, scalable algorithms, and multimodality integration are still poorly understood. This project will provide a suite of analytical and computational tools addressing these challenges. The research will follow three synergistic directions. First, basic framework development will produce statistical models and optimization algorithms that account for the unique traits of ecological networks. Second, the researchers will put forth solutions for highly challenging issues like link prediction under limited resources and species competition. Third, the team will provide a systematic evaluation plan for the problems of interest. Designing effective statistical models and robust, scalable algorithms for ecological networks is well-motivated for both modern ecology research and computer science. This project will lay out the foundations for ecological network analytics by leveraging modern data science tools such as low-rank matrix/tensor factorization, graphical models, and neural 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.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
Standard Grant (Standard)
Application #
Program Officer
Sylvia Spengler
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Oregon State University
United States
Zip Code