Networks at Your Fingertips: On Managing and Summarizing Big Graphs

Abstract

Modern science and technology have witnessed in the past decade an explosive growth of information networks giving rise to a vast ocean of graph-structured data with completely transformed scale, structure, and complexity. In this project, the Pl proposes a series of graph management and summarization solutions to simplifying real-world big graphs of extreme scale, heterogeneity, and dynamics into concise, structure-enriched, and quality-preserving graph summaries, within which the crucial properties and salient structural/content insight of original graphs can be precisely retained, or even enhanced, thus enabling efficient, cost-effective, scalable, and interactive graph-based computation and exploitation for large-scale information networks. The PI will systematically investigate the principles, methodologies, and algorithms for managing and summarizing big graph-structured data, and design novel, quality-guaranteed graph sparsification, aggregation, and sketching methods towards summarizing massive, heterogeneous, and dynamic big graphs. The PI will build on his extensive background in graph management and mining to develop new graph summarization theory and techniques, and integrate them into a prototype graph summarization system gSummary, that can be deployed into different phases of graph processing pipelines for managing and summarizing real-world big graphs. This project will open a new research frontier for managing, exploring, and understanding big graphs, facilitate the widespread availability of information networks, and result in efficient, cost-effective, and scalable big graph management, access, and exploitation for the Army Research Office (ARO) and the whole society. It will also inspire further investigation and development of data summarization methodologies and techniques, thus leading to new scientific inquiries and advances in the broad directions of big data management and data-intensive computation in today s highly networked world.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 14, 2019
Source ID
W911NF1810395

Entities

People

  • Peixiang Zhao

Organizations

  • Army Contracting Command
  • Florida State University
  • United States Army

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Graph Algorithms and Convex Optimization.