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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...

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Detalles Bibliográficos
Autores principales: Yang, Hailu, Zhang, Jin, Ding, Xiaoyu, Chen, Chen, Wang, Lili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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