Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2020

10.06.2020

Finding Best Matching Community for Common Nodes in Mobile Social Networks

verfasst von: Muluneh Mekonnen Tulu, Ronghui Hou, Shambel Aregay Gerezgiher, Talha Younas, Melkamu Deressa Amentie

Erschienen in: Wireless Personal Communications | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The increase of mobile data users has created traffic congestion in current cellular networks. Due to this, mobile network providers have been facing difficulty in delivering the best services for customers. Since, detecting community in mobile social network is a valuable technique to leverage the downlink traffic congestion by enhancing local communications within the community, it attracts the attention of many researchers. Therefore, developing an algorithm, which detects community, plays a key role in mobile social network. In this paper, first, we proposed external density metrics to detect mobile social network. External density is defined as the ratio of outgoing links to total links of the community. Second, method to find the best group for common node is proposed. Therefore, an external density algorithm, makes a fair partition by grouping common nodes to a community with relatively higher external density. As a result, the overall modularity value of the network has increased. Third, the proposed algorithm is evaluated. Hence, the evaluation results confirm that our proposed approach has demonstrated good performance improvements than traditional methods.
Vorheriger Artikel On Formal Modeling and Validation of Wireless Sensor Network Protocols
Nächster Artikel Double Stage Gaussian Filter for Better Underwater Image Enhancement

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Visual, C., & Index, N. (2016). Cisco visual networking index: Global mobile data traffic forecast update. 21.
2.
Didwania, A., & Narmawala, Z. (2015). A comparative study of various community detection algorithms in the mobile social network. In 2015 5th Nirma Univ Int Conf Eng, (pp. 1–6).
3.
Zhang, Y., & Yeung, D. Y. (2012). Overlapping community detection via bounded nonnegative matrix tri-factorization. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining—KDD ’12. New York, New York, USA: ACM Press.
4.
Lin, W., Kong, X., Yu, P. S., Wu, Q., Jia, Y., & Li, C. (2012). Community detection in incomplete information networks. In Proceedings of the 21st international conference on World Wide Web - WWW ’12. (p. 341) New York, USA: ACM Press .
5.
Brodka, P., Saganowski, S., & Kazienko, P. (2013). Group evolution discovery in social networks. arXiv:​1304.​4137. Accessed 27 Oct 2017.
6.
Cazabet, R., Amblard, F., & Hanachi, C. (2010). Detection of overlapping communities in dynamical social networks. In 2010 IEEE Second international conference on social computing. (pp. 309–314)IEEE .
7.
Stephen, P. B., & Martin, G. E. (2000). Models of core/periphery structures. Social Networks, 21, 375–395.
8.
Snyder, D., & Kick, E. L. (1979). Structural position in the world system and economic growth, 1955–1970: A multiple-network analysis of transnational interactions. American Journal of Sociology, 84(5), 1096–1126.
9.
Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499.
10.
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Elsevier, Amsterdam
11.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.MATH
12.
Chobe, S., & Zhan, J. (2019). Advancing community detection using keyword attribute search. Journal of Big Data, 6(1), 83.
13.
Wu, L., Zhang, Q., & Chen, C. H. (2019). Deep community detection method for social networks.
14.
Chamberlain, B. P., Levy-Kramer, J., Humby, C., & Deisenroth, M. P. (2018). Real-time community detection in full social networks on a laptop. PloS ONE, 13(1), e0188702.
15.
Gu, K., Liu, D., & Wang, K. (2019). Social community detection scheme based on social-aware in mobile social networks. IEEE Access, 7, 173407–173418.
16.
Rahiminejad, S., Maurya, M. R., & Subramaniam, S. (2019). Topological and functional comparison of community detection algorithms in biological networks. BMC Bioinformatics, 20(1), 212.
17.
Jiang, J., Zhang, S., Li, B., & Li, B. (2016). Maximized cellular traffic offloading via device-to-device content sharing. IEEE Journal on Selected Areas in Communications, 34, 82–91.
18.
Deressa Amentie, M., Sheng, M., Song, J., & Liu, J. (2016). Minimum delay guaranteed cooperative device-to-device caching in 5G wireless networks. In 2016 8th International conference on wireless communications and signal processing (WCSP). (pp. 1–5)IEEE.
19.
Zhou, Z., & Zhang, X. (2015). Directional antenna-based single channel full duplex. IET Communications, 9, 1999–2006.
20.
Didwania, A., & Narmawala, Z. (2016). A comparative study of various community detection algorithms in the mobile social network. In NUiCONE 2015-5th Nirma Univ Int Conf Eng.
21.
Newman, M. E., & Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68(3), 036122.
22.
Boujlaleb, L., Idarrou, A., Mammass, D., & Sarr, I. (2015). User-centric approach of detecting temporary community. In 2015 Third world conference on complex systems (WCCS). (pp. 1–6)IEEE .
23.
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 26113.
24.
Xu, X., Yuruk, N., Feng, Z., & Schweiger, T. A. J. (2007). SCAN: A structural clustering algorithm for networks. 13th ACM SIGKDD Int conf knowl discov data min. (pp. 824–833)
25.
Ahn, Y.-Y., Bagrow, J. P., & Lehmann, S. (2010). Link communities reveal multiscale complexity in networks. Nature, 466, 761–764.
26.
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888–905.
27.
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of National Academy of Sciences, 99, 7821–7826.MathSciNetMATH
28.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174.MathSciNet
29.
Piccardi, C. (2011). Finding and testing network communities by lumped Markov chains. PLoS ONE, 6, e27028.
30.
Darst, R., Reichman, D., Ronhovde, P., & Nussinov, Z. (2013). An edge density definition of overlapping and weighted graph communities.
31.
Ronhovde, R. K., Peter, R., & Nussinov, Z. (2013). An edge density definition of overlapping and weighted graph communities, arXiv preprint arXiv:​1301.​3120.
32.
Caputo, A., Socievole, A., & De Rango, F. (2015). CRAWDAD dataset unical/socialblueconn (v. 2015-02-08). CRAWDAD Wirel Netwdata Arch.
33.
Zhe, C., Sun, A., & Xiao, X. (2019). Community detection on large complex attribute network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2041–2049).
34.
Rigi, M. A., Moser, I., & Farhangi, M. M. (2017). Derivatives in graph space with applications for finding and tracking local communities. In IEEE/ACM International conference on advances in social networks analysis and mining (pp. 79–107). Springer, Cham.
35.
Ozturk, K., Polat, F., & Özyer, T. (2017). An evolutionary approach for detecting communities in social networks. In IEEE/ACM International conference on advances in social networks analysis and mining (pp. 17–44). Springer, Cham.
36.
Lei, J., Chen, K., & Lynch, B. (2020). Consistent community detection in multi-layer network data. Biometrika, 107(1), 61–73.MathSciNetMATH
37.
Newman, M. E. J., Cantwell, G. T., & Young, J. G. (2020). Improved mutual information measure for clustering, classification, and community detection. Physical Review E, 101(4), 042304.MathSciNet
38.
Galvan, G., & Agarwal, J. (2018). Community detection in action: Identification of critical elements in infrastructure networks. Journal of Infrastructure Systems, 24(1), 04017046.
39.
Zhao, Z., Zheng, S., Li, C., Sun, J., Chang, L., & Chiclana, F. (2018). A comparative study on community detection methods in complex networks. Journal of Intelligent and Fuzzy Systems, 35(1), 1077–1086.
40.
Tripathi, B., Parthasarathy, S., Sinha, H., Raman, K., & Ravindran, B. (2019). Adapting community detection algorithms for disease module identification in heterogeneous biological networks. Frontiers in Genetics, 10, 164.
41.
Karataş, A., & Şahin, S. (2018). Application areas of community detection: A review. In 2018 International congress on big data, deep learning and fighting cyber terrorism (IBIGDELFT) (pp. 65–70). IEEE.
42.
Liu, Z., Wang, H., Cheng, L., Peng, W., & Li, X. (2019). Temporal label walk for community detection and tracking in temporal network. Applied Sciences, 9(15), 3199.
43.
Laib, M., Guignard, F., Kanevski, M., & Telesca, L. (2019). Community detection analysis in wind speed-monitoring systems using mutual information-based complex network. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(4), 043107.
44.
Abbe, E. (2017). Community detection and stochastic block models: Recent developments. The Journal of Machine Learning Research, 18(1), 6446–6531.MathSciNet
45.
Berahmand, K., Bouyer, A., & Vasighi, M. (2018). Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Transactions on Computational Social Systems, 5(4), 1021–1033.
46.
Zhu, P., Dai, X., Li, X., Gao, C., Jusup, M., & Wang, Z. (2019). Community detection in temporal networks via a spreading process. EPL (Europhysics Letters), 126(4), 48001.
47.
Zhe, C., Sun, A., & Xiao, X. (2019). Community detection on large complex attribute network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2041–2049).
48.
Geng, J., Bhattacharya, A., & Pati, D. (2019). Probabilistic community detection with unknown number of communities. Journal of the American Statistical Association, 114(526), 893–905.MathSciNetMATH
49.
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 26113.
50.
Rombach, M. P., Porter, M. A., Fowler, J. H., & Mucha, P. J. (2014). Core-periphery structure in networks. SIAM Journal on Applied Mathematics, 74, 167–190.MathSciNetMATH
51.
Lancichinetti, A., & Fortunato, S. (2011). Limits of modularity maximization in community detection. Physical Review E, 84, 66122.
52.
Good, B. H., De Montjoye, Y. A., & Clauset, A. (2010). Performance of modularity maximization in practical contexts. Physical Review E, 81(4), 046106.MathSciNet
53.
Agarwal, G., & Kempe, D. (2008). Modularity-maximizing graph communities via mathematical programming. European Physical Journal B: Condensed Matter and Complex Systems, 66, 409–418.MathSciNetMATH
54.
Sales-Pardo, M., Guimera, R., Moreira, A. A., & Amaral, L. A. N. (2007). Extracting the hierarchical organization of complex systems. Proceedings of National Academy of Sciences, 104, 15224–15229.
55.
Wakita, K., & Tsurumi, T. (2007). Finding community structure in mega-scale social networks. In Proceedings of the 16th international conference on World Wide Web - WWW ’07.(p. 1275) New York, USA: ACM Press.
56.
Shiga, M., Takigawa, I., & Mamitsuka, H. (2007). A spectral clustering approach to optimally combining numerical vectors with a modular network. Proc 13th ACM SIGKDD int conf knowl discov data min. (pp. 647–656).
57.
Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36–41.
58.
PEARSON, K. (1905). The problem of the random walk. Nature, 72, 318–318.
59.
Newman, M. E. J. (2010). Mark Newman-networks—an introduction. Oxford: Oxford University Press.
60.
Barrat, A., & Barthelemy AV, M. (2009). Dynamical processes on complex networks. Journal of Statistical Physics, 135, 773–74.
61.
Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33, 452–473.
62.
63.
64.
Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences USA, 105, 1118–1123.
65.
Rosvall, M., Axelsson, D., & Bergstrom, C. T. (2009). The map equation. The European Physical Journal Special Topics, 178, 13–23.
66.
67.
Meyer, C. D. (2000). Matrix Analysis and Applied Linear Algebra (solutions), 1026, 14–18.
Metadaten
Titel
Finding Best Matching Community for Common Nodes in Mobile Social Networks
verfasst von
Muluneh Mekonnen Tulu
Ronghui Hou
Shambel Aregay Gerezgiher
Talha Younas
Melkamu Deressa Amentie
Publikationsdatum
10.06.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07508-7

Weitere Artikel der Ausgabe 4/2020

Wireless Personal Communications 4/2020 Zur Ausgabe

OriginalPaper

A New Lightweight User Authentication and Key Agreement Scheme for WSN

OriginalPaper

Towards the Design of a Stronger AES: AES with Key Dependent Shift Rows (KDSR)

OriginalPaper

A Bernoulli Optimal Kalman Filter for a Multi-sensor System with Random Data Packet Dropouts and Delays

OriginalPaper

The Responses of Ionospheric Conductivities on the Mid-Latitudes to Changes in the BZ Component of Interplanetary Magnetic Field

OriginalPaper

Hexagonal Clustered Trust Based Distributed Group Key Agreement Scheme in Mobile Ad Hoc Networks

OriginalPaper

Improved Network Traffic by Attacking Denial of Service to Protect Resource Using Z-Test Based 4-Tier Geomark Traceback (Z4TGT)

Neuer Inhalt

    Bildnachweise