Network Semantic Segmentation With Application To Github
Keywords
Community detection; Github social coding; Social networks
Abstract
In this paper we introduce the concept of network semantic segmentation for social network analysis. We consider the GitHub social coding network which has been a center of attention for both researchers and software developers. Network semantic segmentation describes the process of associating each user with a class label such as a topic of interest. We augment node attributes with network significant connections and then employ machine learning approaches to cluster the users. We compare the results with a network segmentation performed using community detection algorithms and one executed by clustering with node attributes. Results are compared in terms of community diversity within the semantic segments along with topic coverage.
Publication Date
12-1-2018
Publication Title
Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
Number of Pages
1281-1284
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/CSCI46756.2018.00247
Copyright Status
Unknown
Socpus ID
85078514278 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85078514278
STARS Citation
Hajiakhoond Bidoki, Neda and Sukthankar, Gita, "Network Semantic Segmentation With Application To Github" (2018). Scopus Export 2015-2019. 8902.
https://stars.library.ucf.edu/scopus2015/8902