• A semantic overlapping communi...

Result: A semantic overlapping community detection algorithm based on field sampling

Title:
A semantic overlapping community detection algorithm based on field sampling
Authors:
YU XIN, JING YANG, XIE, Zhi-Qiang
Source:
Expert systems with applications. 42(1):366-375
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Recherche information. Graphe, Information retrieval. Graph, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Psychologie. Psychophysiologie, Psychology. Psychophysiology, Psychologie sociale, Social psychology, Interactions sociales. Communication. Processus de groupe, Social interactions. Communication. Group processes, Attribution, perception et cognition sociale, Social attribution, perception and cognition, Psychologie. Psychanalyse. Psychiatrie, Psychology. Psychoanalysis. Psychiatry, Attributioné perception et cognition sociale, Amas, Cluster, Montón, Analyse amas, Cluster analysis, Analisis cluster, Analyse donnée, Data analysis, Análisis datos, Analyse sémantique, Semantic analysis, Análisis semántico, Chevauchement, Overlap, Imbricación, Classification, Clasificación, Cognition sociale, Social cognition, Cognición social, Communauté virtuelle, Virtual community, Comunidad virtual, Connectivité graphe, Graph connectivity, Conectividad grafo, Echantillonnage, Sampling, Muestreo, Efficacité, Efficiency, Eficacia, Faisabilité, Feasibility, Practicabilidad, Intersection, Intersección, Modélisation, Modeling, Modelización, Représentation connaissance, Knowledge representation, Representación conocimientos, Réseau social, Social network, Red social, Réseau sémantique, Semantic network, Red semántica, Système modulaire, Modular system, Sistema modular, Sémantique, Semantics, Semántica, Community detection, Overlapping communities, Semantic modularity, Semantic social network
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
College of Computer Science and Technology, Harbin Engineering University, Heilongjiang 150001, China
College of Computer Science and Technology, Harbin University of Science and Technology, Heilongjiang 150001, China
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843408
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Psychology. Ethology

FRANCIS
Accession Number:
edscal.28843408
Database:
PASCAL Archive

Further Information

The traditional semantic social network (SSN) community detection algorithms need to preset the number of the communities and could not detect the overlapping communities. To solve the issue of presetting the number of communities, we present a clustering algorithm for community detection based on the link-field-topic (LFT) model suggested. For the process of clustering is independent of context sampling, the number of communities is not necessary to be preset. To solve the issue of overlapping community detection, we establish the semantic link weight (SLW) depending on the analysis of LFT, to evaluate the semantic weight of links for each sampling field. The proposed clustering algorithm is based on the SLW which could separate the SSN into clustering units. As a result, the intersection on several units is the overlapping part. Finally, we establish semantic modularity (SQ) involving SQ1 and SQ2 for the evaluation of the detected semantic communities. The efficiency and feasibility of the LFT model and the semantic modularity is verified by experimental analysis.