Mining Large Dynamic Graphs and Tensors

Abstract

Graphs are ubiquitous, representing a variety of information, ranging from who follows whom on online social networks to who reviews what on e-commerce sites. Many of these graphs are large (e.g., online social networks with over two billion active users) and dynamic (i.e., nodes and edges can be added and removed over time). Moreover, they are with rich side information (e.g., e-commerce reviews with timestamps, ratings, and text) and thus naturally modeled as tensors (i.e., multi-dimensional arrays).

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Document Details

Document Type
Technical Report
Publication Date
Feb 02, 2019
Accession Number
AD1168000

Entities

People

  • Kijung Shin

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Change Detection
  • Computational Science
  • Computer Networks
  • Computers
  • Data Mining
  • Electronic Commerce
  • Information Processing
  • Information Science
  • Information Systems
  • Internet
  • Knowledge Management
  • Machine Learning
  • Network Science
  • Social Media
  • Social Networking Services
  • Social Networks

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Government Contracting/Procurement.
  • Graph Algorithms and Convex Optimization.