Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/19/7/360 |
| _version_ | 1828276221390094336 |
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| author | Khaled Almgren Minkyu Kim Jeongkyu Lee |
| author_facet | Khaled Almgren Minkyu Kim Jeongkyu Lee |
| author_sort | Khaled Almgren |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:54:56Z |
| format | Article |
| id | doaj.art-6255fb3a022c40fa89ff2e568f97adbb |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:54:56Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-6255fb3a022c40fa89ff2e568f97adbb2022-12-22T02:57:16ZengMDPI AGEntropy1099-43002017-07-0119736010.3390/e19070360e19070360Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data AnalysisKhaled Almgren0Minkyu Kim1Jeongkyu Lee2Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06614, USAASML, 77 Danbury RD, Wilton, CT 06897, USAComputer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06614, USATopological 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.https://www.mdpi.com/1099-4300/19/7/360topologytopological data analysisgeometrysocial networks analysis and mininghigh-dimensional data analysis |
| spellingShingle | Khaled Almgren Minkyu Kim Jeongkyu Lee Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis Entropy topology topological data analysis geometry social networks analysis and mining high-dimensional data analysis |
| title | Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis |
| title_full | Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis |
| title_fullStr | Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis |
| title_full_unstemmed | Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis |
| title_short | Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis |
| title_sort | extracting knowledge from the geometric shape of social network data using topological data analysis |
| topic | topology topological data analysis geometry social networks analysis and mining high-dimensional data analysis |
| url | https://www.mdpi.com/1099-4300/19/7/360 |
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