Next Article in Journal
Competitive Sharing of Spectrum: Reservation Obfuscation and Verification Strategies
Next Article in Special Issue
Intrinsic Losses Based on Information Geometry and Their Applications
Previous Article in Journal
Non-Linear Stability Analysis of Real Signals from Nuclear Power Plants (Boiling Water Reactors) Based on Noise Assisted Empirical Mode Decomposition Variants and the Shannon Entropy
Previous Article in Special Issue
α-Connections and a Symmetric Cubic Form on a Riemannian Manifold
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(7), 360; https://doi.org/10.3390/e19070360

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

1
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06614, USA
2
ASML, 77 Danbury RD, Wilton, CT 06897, USA
*
Author to whom correspondence should be addressed.
Received: 13 May 2017 / Revised: 10 July 2017 / Accepted: 14 July 2017 / Published: 14 July 2017
(This article belongs to the Special Issue Information Geometry II)
Full-Text   |   PDF [1349 KB, uploaded 20 July 2017]   |  

Abstract

Topological data analysis is a noble approach to extract meaningful information from high-dimensional data and is robust to noise. It is based on topology, which aims to study the geometric shape of data. In order to apply topological data analysis, an algorithm called mapper is adopted. The output from mapper is a simplicial complex that represents a set of connected clusters of data points. In this paper, we explore the feasibility of topological data analysis for mining social network data by addressing the problem of image popularity. We randomly crawl images from Instagram and analyze the effects of social context and image content on an image’s popularity using mapper. Mapper clusters the images using each feature, and the ratio of popularity in each cluster is computed to determine the clusters with a high or low possibility of popularity. Then, the popularity of images are predicted to evaluate the accuracy of topological data analysis. This approach is further compared with traditional clustering algorithms, including k-means and hierarchical clustering, in terms of accuracy, and the results show that topological data analysis outperforms the others. Moreover, topological data analysis provides meaningful information based on the connectivity between the clusters. View Full-Text
Keywords: topology; topological data analysis; geometry; social networks analysis and mining; high-dimensional data analysis topology; topological data analysis; geometry; social networks analysis and mining; high-dimensional data analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Almgren, K.; Kim, M.; Lee, J. Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis. Entropy 2017, 19, 360.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top