SHF: Small: Towards High-Performance Machine Learning on Graphs

The world has become increasingly intertwined through social networks, smart phones, and more generally cyber-physical systems. Connected people, devices, and systems introduce a pressing research challenge, that is, the need of understanding the relationships between different entities in dynamic networks. As graphs with vertices and edges are a natural representation of such relationships, machine learning on these graphs (networks) is beneficial to a variety of applications spanning from biology, finance, to social science.

Importantly, these networks grow exponentially in size. As a result, one needs to not only store and manage large-scale, dynamic graph datasets, but also support fast execution of machine-learning tasks.

This project designs and develops a new set of high-performance machine-learning algorithms and systems for big graph datasets. The research goal is achieved through systems-level innovations for enabling graph-based machine learning on high-performance computing systems, coupled with the development of new scalable, robust algorithms for graph extraction and learning.

The new graph learning system will significantly advance the state of the art in machine learning on graphs, delivering new tools to an array of scientific disciplines from social to life sciences. A number of undergraduate and graduate students, including those from under-represented groups, will participate in this project. The research results will be disseminated as open-source projects, as well as technical publications in major conferences and scientific journals.

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.