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GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection
Community detection in semantic social networks is a crucial issue in online social network analysis, and has received extensive attention from researchers in various fields. Different conventional methods discover semantic communities based merely on users’ preferences towards global topics, ignori...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498074/ https://www.ncbi.nlm.nih.gov/pubmed/36141160 http://dx.doi.org/10.3390/e24091274 |
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author | Yang, Hailu Zhang, Jin Ding, Xiaoyu Chen, Chen Wang, Lili |
author_facet | Yang, Hailu Zhang, Jin Ding, Xiaoyu Chen, Chen Wang, Lili |
author_sort | Yang, Hailu |
collection | PubMed |
description | Community detection in semantic social networks is a crucial issue in online social network analysis, and has received extensive attention from researchers in various fields. Different conventional methods discover semantic communities based merely on users’ preferences towards global topics, ignoring the influence of topics themselves and the impact of topic propagation in community detection. To better cope with such situations, we propose a Gaming-based Topic Influence Percolation model (GTIP) for semantic overlapping community detection. In our approach, community formation is modeled as a seed expansion process. The seeds are individuals holding high influence topics and the expansion is modeled as a modified percolation process. We use the concept of payoff in game theory to decide whether to allow neighbors to accept the passed topics, which is more in line with the real social environment. We compare GTIP with four traditional (GN, FN, LFM, COPRA) and seven representative (CUT, TURCM, LCTA, ACQ, DEEP, BTLSC, SCE) semantic community detection methods. The results show that our method is closer to ground truth in synthetic networks and has a higher semantic modularity in real networks. |
format | Online Article Text |
id | pubmed-9498074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94980742022-09-23 GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection Yang, Hailu Zhang, Jin Ding, Xiaoyu Chen, Chen Wang, Lili Entropy (Basel) Article Community detection in semantic social networks is a crucial issue in online social network analysis, and has received extensive attention from researchers in various fields. Different conventional methods discover semantic communities based merely on users’ preferences towards global topics, ignoring the influence of topics themselves and the impact of topic propagation in community detection. To better cope with such situations, we propose a Gaming-based Topic Influence Percolation model (GTIP) for semantic overlapping community detection. In our approach, community formation is modeled as a seed expansion process. The seeds are individuals holding high influence topics and the expansion is modeled as a modified percolation process. We use the concept of payoff in game theory to decide whether to allow neighbors to accept the passed topics, which is more in line with the real social environment. We compare GTIP with four traditional (GN, FN, LFM, COPRA) and seven representative (CUT, TURCM, LCTA, ACQ, DEEP, BTLSC, SCE) semantic community detection methods. The results show that our method is closer to ground truth in synthetic networks and has a higher semantic modularity in real networks. MDPI 2022-09-09 /pmc/articles/PMC9498074/ /pubmed/36141160 http://dx.doi.org/10.3390/e24091274 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Hailu Zhang, Jin Ding, Xiaoyu Chen, Chen Wang, Lili GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection |
title | GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection |
title_full | GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection |
title_fullStr | GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection |
title_full_unstemmed | GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection |
title_short | GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection |
title_sort | gtip: a gaming-based topic influence percolation model for semantic overlapping community detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498074/ https://www.ncbi.nlm.nih.gov/pubmed/36141160 http://dx.doi.org/10.3390/e24091274 |
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