Skip to main content

Data Mining When Each Data Point is a Network

Author(s): Rajendran, K; Kattis, A; Holiday, A; Kondor, R; Kevrekidis, Yannis G.

To refer to this page use:
Abstract: © Springer International Publishing AG 2017. We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges in mining graph data is the definition of a suitable pairwise similarity metric in the space of graphs. We explore two practical solutions to solving this problem: one based on finding subgraph densities, and one using spectral information. The approach is illustrated on three test data sets (ensembles of graphs); two of these are obtained from standard literature graph generating algorithms, while the graphs in the third example are sampled as dynamic snapshots from an evolving network simulation. We further combine these approaches with equation free techniques, demonstrating how such data mining can enhance scientific computation of network evolution dynamics.
Publication Date: 1-Jan-2017
Citation: Rajendran, K, Kattis, A, Holiday, A, Kondor, R, Kevrekidis, IG. (2017). Data Mining When Each Data Point is a Network. Springer Proceedings in Mathematics and Statistics, 205 (289 - 317). doi:10.1007/978-3-319-64173-7_17
DOI: doi:10.1007/978-3-319-64173-7_17
ISSN: 2194-1009
EISSN: 2194-1017
Pages: 289 - 317
Type of Material: Journal Article
Journal/Proceeding Title: Springer Proceedings in Mathematics and Statistics
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.