Accession Number:

AD1168000

Title:

Mining Large Dynamic Graphs and Tensors

Descriptive Note:

[Technical Report, Doctoral Thesis]

Corporate Author:

Carnegie Mellon University

Personal Author(s):

• Shin, Kijung

Report Date:

2019-02-02

Pagination or Media Count:

321

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.

Descriptors:

Subject Categories:

• Information Science
• Numerical Mathematics

Distribution Statement:

[A, Approved For Public Release]