Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2021

214  University of North Texas  (84510)

Principal Investigator: Bhowmick,Sanjukta

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 320,042

Exceeds $250,000 (Is it flagged?): Yes

Start and End Dates: - 6/30/23

Restricted Research: YES

Academic Discipline: Computer Science & Engineering

Department, Center, School, or Institute: College of Engineering

Title of Contract, Award, or Gift: Collaborative Research: SHF Core: Medium: NetSplicer: Scalable Decoupling-based Algorithms for Multilayer Network Analysis

Name of Granting or Contracting Agency/Entity: National Science Foundation

Program Title: N/A
CFDA Linked: Computer and Information Science and Engineering


1.1.1 (SAM); Multilayer networks have recently emerged as an expressive formalism for modeling and analysis of multi-featured multi-relational data. Algorithm design and software development for multilayer network analysis are still in their infancy. It is challenging to develop scalable algorithms for analyzing multilayer networks because, while analysis of single networks is based only on their structure, analysis of multilayer networks have added constraints imposed by the different types of entities and relations. This projects aims to develop NetSplicer, the first collection of scalable high-performance algorithms for multilayer network analysis. The approaches in NetSplicer will be based on a divide-and-conquer-like technique called network decoupling. Using decoupling, the multilayer network can be subdivided into multiple components, each of which can be potentially analyzed using existing algorithms for single layer networks. Network decoupling addresses many of the issues in the current approaches to multilayer analysis, such as reducing loss of information and preserving structural and semantic information. The challenges in efficiently applying network decoupling includes (i) determining optimal decoupling strategies, (ii) preserving the structure and content of multilayer networks that have multiple vertex and edge types, and (iii) developing architecture-aware scalable algorithms, that apply across different layers of the multilayer network. Algorithms will be developed for several popular analysis objectives including connectivity, centrality computation, community detection, subgraph mining, and subgraph querying. Their accuracy will be validated by (a) comparing with results of sequential algorithms for multilayer networks, and (b) collaborating with domain scientists to evaluate the quality of the results. To facilitate the study of multilayer networks, the PIs will also make available a repository of multi-featured datasets, along with functions to create multilayer networks from these datasets. Keywords: multilayer networks; scalable algorithms; high-performance computing; architecture-aware design.

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