USNAP: fast unique dense region detection and its application to lung cancer.

ABSTRACT:

Motivation

Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains.

Results

There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs tha

SUBMITTER: Wong SWH 

PROVIDER: S-EPMC10425186 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature