Network Semantic Segmentation with Application to GitHub
CSCI 2018: International Conference on Computational Science and Computational Intelligence 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...
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| creator | Bidoki, Neda Hajiakhoond Sukthankar, Gita |
| description | CSCI 2018: International Conference on Computational Science and
Computational Intelligence 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 |
| doi_str_mv | 10.48550/arxiv.1902.05220 |
| format | Article |
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Computational Intelligence 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</description><identifier>DOI: 10.48550/arxiv.1902.05220</identifier><language>eng</language><subject>Computer Science - Social and Information Networks</subject><creationdate>2019-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1902.05220$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1902.05220$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Bidoki, Neda Hajiakhoond</creatorcontrib><creatorcontrib>Sukthankar, Gita</creatorcontrib><title>Network Semantic Segmentation with Application to GitHub</title><description>CSCI 2018: International Conference on Computational Science and
Computational Intelligence 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</description><subject>Computer Science - Social and Information Networks</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tuwjAURL1hUVE-oCvyA0l97dixlxGigIRgAfvoOrHBal5KXR5_z3M1o1nMzCHkC2iSKiHoNw4Xf0pAU5ZQwRj9IGpjw7kbfqOdbbANvrybQ2PbgMF3bXT24RjlfV_78hWELlr4sPw3n2TksP6zk7eOyf5nvp8t4_V2sZrl6xhlRuNUmvsuKEt15qw2hulMg5bomDRYAjgheKUql1ImhALFgaNKIUMLla0kH5Ppq_Z5vegH3-BwLR4IxROB3wB6FkBD</recordid><startdate>20190213</startdate><enddate>20190213</enddate><creator>Bidoki, Neda Hajiakhoond</creator><creator>Sukthankar, Gita</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20190213</creationdate><title>Network Semantic Segmentation with Application to GitHub</title><author>Bidoki, Neda Hajiakhoond ; Sukthankar, Gita</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-46b55018e097fe9bb2979196af26bac11f553d8df40255818313a8417ae1ded63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Social and Information Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Bidoki, Neda Hajiakhoond</creatorcontrib><creatorcontrib>Sukthankar, Gita</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bidoki, Neda Hajiakhoond</au><au>Sukthankar, Gita</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network Semantic Segmentation with Application to GitHub</atitle><date>2019-02-13</date><risdate>2019</risdate><abstract>CSCI 2018: International Conference on Computational Science and
Computational Intelligence 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</abstract><doi>10.48550/arxiv.1902.05220</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.1902.05220 |
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| language | eng |
| recordid | cdi_arxiv_primary_1902_05220 |
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| subjects | Computer Science - Social and Information Networks |
| title | Network Semantic Segmentation with Application to GitHub |
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