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Ranking community detection algorithms for complex social networks using multilayer network design approach

Ranking community detection algorithms for complex social networks using multilayer network... Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.Design/methodology/approachIn this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.FindingsIn the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.Research limitations/implicationsIn this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.Practical implicationsProposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.Social implicationsCommunity detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.Originality/valueRanking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

Ranking community detection algorithms for complex social networks using multilayer network design approach

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1744-0084
eISSN
1744-0084
DOI
10.1108/ijwis-02-2022-0040
Publisher site
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Abstract

Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.Design/methodology/approachIn this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.FindingsIn the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.Research limitations/implicationsIn this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.Practical implicationsProposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.Social implicationsCommunity detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.Originality/valueRanking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

Journal

International Journal of Web Information Systems – Emerald Publishing

Published: Dec 12, 2022

Keywords: Community detection; Multilayer social networks; Heterogeneous information; Community scoring functions; Partition comparison; MCDM

References

  • Exploring the limits of community detection strategies in complex networks
    Aldecoa, R.; Marín, I.
  • A cooperative evolutionary approach to learn communities in multilayer networks
    Amelio, A.; Pizzuti, C.
  • Community detection in large-scale social networks: state-of-the-art and future directions
    Azaouzi, M.; Rhouma, D.; Romdhane, L.B.
  • Identifying the community structure of the international-trade multi-network
    Barigozzi, M.; Fagiolo, G.; Mangioni, G.
  • Dynamic reconfiguration of human brain networks during learning
    Bassett, D.S.; Wymbs, N.F.; Porter, M.A.; Mucha, P.J.; Carlson, J.M.; Grafton, S.T.
  • Finding redundant and complementary communities in multidimensional networks
    Berlingerio, M.; Coscia, M.; Giannotti, F.
  • Abacus: frequent pattern mining-based community discovery in multidimensional networks
    Berlingerio, M.; Pinelli, F.; Calabrese, F.
  • Link-prediction enhanced consensus clustering for complex networks
    Burgess, M.; Adar, E.; Cafarella, M.
  • Mcg-webv: a benchmark dataset for web video analysis
    Cao, J.; Zhang, Y.-D.; Song, Y.-C.; Chen, Z.-N.; Zhang, X.; Li, J.-T.
  • An steep-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: a case study from India
    Choudhary, D.; Shankar, R.
  • Practical nonparametric statistics
    Conover, W.J.; Conover, W.J.
  • Comparing community structure identification
    Danon, L.; Diaz-Guilera, A.; Duch, J.; Arenas, A.
  • Navigability of interconnected networks under random failures
    De Domenico, M.; Solé-Ribalta, A.; Gómez, S.; Arenas, A.
  • Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems
    De Domenico, M.; Lancichinetti, A.; Arenas, A.; Rosvall, M.
  • Clustering with multi-layer graphs: a spectral perspective
    Dong, X.; Frossard, P.; Vandergheynst, P.; Nefedov, N.
  • Community detection in graphs
    Fortunato, S.
  • Diffusion dynamics on multiplex networks
    Gomez, S.; Diaz-Guilera, A.; Gomez-Gardenes, J.; Perez-Vicente, C.J.; Moreno, Y.; Arenas, A.
  • Tracking the evolution of communities in dynamic social networks
    Greene, D.; Doyle, D.; Cunningham, P.
  • Community detection as an inference problem
    Hastings, M.B.
  • Community detection in multiplex networks: a seed-centric approach
    Hmimida, M.; Kanawati, R.
  • A comparative study of modularity-based community detection methods for online social networks
    Karataş, A.; Şahin, S.
  • A new measure of rank correlation
    Kendall, M.G.
  • Community detection in multiplex networks using locally adaptive random walks
    Kuncheva, Z.; MT, G.
  • Consensus clustering in complex networks
    Lancichinetti, A.; Fortunato, S.
  • Empirical comparison of algorithms for network community detection
    Leskovec, J.; Lang, K.J.; Mahoney, M.
  • The function of communities in protein interaction networks at multiple scales
    Lewis, A.C.; Jones, N.S.; Porter, M.A.; Deane, C.M.
  • Community structure in time-dependent, multiscale, and multiplex networks
    Mucha, P.J.; Richardson, T.; Macon, K.; Porter, M.A.; Onnela, J.-P.
  • Detecting community structure in networks
    Newman, M.E.
  • Modularity and community structure in networks
    Newman, M.E.
  • Spectral methods for community detection and graph partitioning
    Newman, M.E.
  • A declarative model assembly infrastructure for verification and validation
    North, M.J.; Howe, T.R.; Collier, N.T.; Vos, J.R.
  • Network analysis reveals sex-and antibiotic resistance-associated antivirulence targets in clinical uropathogens
    Parker, K.S.; Wilson, J.D.; Marschall, J.; Mucha, P.J.; Henderson, J.P.
  • Communities in networks
    Porter, M.A.; Onnela, J.-P.; Mucha, P.J.
  • Discovering community structure in multilayer networks
    Pramanik, S.; Tackx, R.; Navelkar, A.; Guillaume, J.-L.; Mitra, B.
  • Abrupt transition in the structural formation of interconnected networks
    Radicchi, F.; Arenas, A.
  • On clustering on graphs with multiple edge types
    Rocklin, M.; Pinar, A.
  • A novel approach to evaluate community detection algorithms on ground truth
    Rossetti, G.; Pappalardo, L.; Rinzivillo, S.
  • CDLIB: a python library to extract, compare and evaluate communities from complex networks
    Rossetti, G.; Milli, L.; Cazabet, R.
  • Normalized cuts and image segmentation
    Shi, J.; Malik, J.
  • Py3plex: a library for scalable multilayer network analysis and visualization
    Škrlj, B.; Kralj, J.; Lavrač, N.; Aiello, L.M.; Cherifi, C.; Cherifi, H.; Lambiotte, R.; Lió, P.; Rocha, L.M.
  • Ensemble-based community detection in multilayer networks
    Tagarelli, A.; Amelio, A.; Gullo, F.
  • Uncovering groups via heterogeneous interaction analysis
    Tang, L.; Wang, X.; Liu, H.
  • Community detection via heterogeneous interaction analysis
    Tang, L.; Wang, X.; Liu, H.
  • Size matters: a comparative analysis of community detection algorithms
    Wagenseller, P.; Wang, F.; Wu, W.
  • Community extraction in multilayer networks with heterogeneous community structure
    Wilson, J.D.; Palowitch, J.; Bhamidi, S.; Nobel, A.B.
  • The lack of a priori distinctions between learning algorithms
    Wolpert, D.H.
  • Defining and evaluating network communities based on ground-truth
    Yang, J.; Leskovec, J.
  • A comparative analysis of community detection algorithms on artificial networks
    Yang, Z.; Algesheimer, R.; Tessone, C.J.