Exploring the structure and function of temporal networks with dynamic graphlets.

ABSTRACT:

Motivation

With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.

Results

We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in sta

SUBMITTER: Hulovatyy Y 

PROVIDER: S-EPMC4765862 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature