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Details of Grant 

EPSRC Reference: EP/T00729X/1
Title: Efficient Spectral Algorithms for Massive and Dynamic Graphs
Principal Investigator: Sun, Dr H
Other Investigators:
Researcher Co-Investigators:
Project Partners:
MPI for Intelligent Systems The Alan Turing Institute University of Cambridge
University of Washington
Department: Sch of Informatics
Organisation: University of Edinburgh
Scheme: EPSRC Fellowship
Starts: 01 January 2020 Ends: 31 December 2024 Value (£): 1,205,706
EPSRC Research Topic Classifications:
Fundamentals of Computing
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
10 Jul 2019 EPSRC ICT Prioritisation Panel July 2019 Announced
10 Sep 2019 ICT and DE Fellowship Interviews 10 and 11 September 2019 Announced
Summary on Grant Application Form
Spectral graph theory investigates the algebraic properties of matrices associated with graphs. Over the past 20 years, studies in spectral graph theory have successfully overcome fundamental bottlenecks faced by combinatorial algorithms, and have become a major research focus in theoretical computer science, machine learning, and network analysis. In particular, some recent breakthrough results show that spectral techniques can be applied to solve central optimisation and learning problems in nearly-linear time. Designing such highly efficient algorithms is crucial to cope with the emergence of massive graphs and real-time data sets coming from technological, social and biological networks.

In this fellowship I propose three research directions to advance the studies of spectral algorithms and their applications in data science: (1) I propose to advance our understanding of fundamental spectral techniques by studying the spectral properties of different matrices representing directed graphs, and exploring new connections between graphs and other mathematical objects, e.g., manifolds studied in geometry; (2) I propose to investigate new spectral algorithms for two fundamental graph problems in different settings, and further improve the state-of-the-art of the two problems with respect to the algorithms' runtime and performance; (3) spectral algorithms vastly outperform combinatorial algorithms with respect to their runtime in the worst case, however the design of most spectral algorithms usually involve many procedures, which bring the issue of numerical stability for efficient implementations. To address this I propose to develop an open-source algorithmic library for spectral algorithms so that by the end of the fellowship data scientists would be able to use the state-of-the-art algorithms for spectral sparsification and graph clustering in a black box manner.

A successful completion of the fellowship will make a significant step towards understanding the power and limits of the algebraic techniques in designing fast graph algorithms in various settings, and the performance of nearly-linear time spectral algorithms in real world data sets.
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