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
Erschienen in:
Probabilistic Nearest Neighbor Query in Traffic-Aware Spatial Networks Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Community Inference with Bayesian Non-negative Matrix Factorization

verfasst von : Xiaohua Shi, Hongtao Lu

Erschienen in: Web Technologies and Applications

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In terms of networks, the clustering is based on the topology structure of the network and the groups found are called Communities. We might expect a coherent group to be one which has more links between members of the group than it has to nodes outside the group in other clusters. Detection Communities in a large network can efficiently simplify network structure, help to understand the network topology and learn how the network works.
As a dimension reduction method, Non-negative Matrix Factorization (NMF) aims to find two non-negative matrices whose product approximates the original matrix well, and is widely used in graph clustering condition with good physical interpretability and universal applicability. Based on the consideration that there is no any physical meaning to reconstruct a network with negative adjacency matrix, using NMF to obtain new representations of network with non-negativity constraints can achieve much productive effect in community analysis.
Incorporating Bayesian methods with prior knowledge for NMF, we can gain further insights into the data and determinate the optimal parameters for detecting model. In this paper, we propose a Bayesian non-negative matrix factorization method with Symmetric assumption (BSNMF), which not only achieve better community detection results in undirected network, but also effectively predict most appropriate count of communities in a large network with Automatic Relevance Determination model. We compare our approaches with other NMF-based methods in Email social networks, and experimental results for community detection show that our approaches are effective to find the communities number and achieve better community detection results.

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

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 "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"

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
Vorheriges Kapitel When a Friend Online is More Than a Friend in Life: Intimate Relationship Prediction in Microblogs
Nächstes Kapitel Maximizing the Influence Ranking Under Limited Cost in Social Network
Fußnoten
Literatur
1.
Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008)MATH
2.
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2008)
3.
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)CrossRef
4.
5.
Cemgil, A.T.: Bayesian inference for nonnegative matrix factorisation models. Comput. Intell. Neurosci. 2009, 1–17 (2009)CrossRef
6.
Ding, Y.: Community detection: topological vs. topical. J. Informetr. 5(4), 498–514 (2011)CrossRef
7.
Fevotte, C., Idier, J.: Algorithms for nonnegative matrix factorization with the beta-divergence. Neural Comput. 23(9), 2421–2456 (2011)MathSciNetCrossRefMATH
8.
Guimerà, R., Danon, L., Díaz Guilera, A., Giralt, F., Arenas, À.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103-1–065103-4 (2003)CrossRef
9.
He, Y.C., Lu, H.T., Huang, L., Shi, X.H.: Non-negative matrix factorization with pairwise constraints and graph laplacian. Neural Process. Lett. 42(1), 167–185 (2015)CrossRef
10.
He, Z., Xie, S., Zdunek, R., Zhou, G., Cichocki, A.: Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering. IEEE Trans. Neural Netw. 22(12), 2117–2131 (2011)CrossRef
11.
Kuang, D., Park, H., Ding, C.H.: Symmetric nonnegative matrix factorization for graph clustering. In: SDM, vol. 12, pp. 106–117. SIAM (2012)
12.
Lai, D., Wu, X., Lu, H., Nardini, C.: Learning overlapping communities in complex networks via non-negative matrix factorization. Int. J. Mod. Phys. C 22(10), 1173–1190 (2011)CrossRefMATH
13.
Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, vol. 13 (2001)
14.
Lee, D., Seung, H., et al.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRef
15.
Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, pp. 631–640. ACM (2010)
16.
Li, T., Ding, C.: The relationships among various nonnegative matrix factorization methods for clustering. In: Sixth International Conference on Data Mining, ICDM 2006, pp. 362–371. IEEE (2006)
17.
Liu, Y., Tennant, D.A., Zhu, Z., Heath, J.K., Yao, X., He, S.: Dime: a scalable disease module identification algorithm with application to glioma progression. PloS one 9(2), e86693:1–e86693:17 (2014)
18.
Mørup, M., Hansen, L.K.: Automatic relevance determination for multi-way models. J. Chemometr. 23(7–8), 352–363 (2009)CrossRef
19.
Newman, M.E.J.: Coauthorship networks and patterns of scientific collaboration. Proc. Natl. Acad. Sci. 101(Suppl. 1), 5200–5205 (2004)CrossRef
20.
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113:1–026113:15 (2004)
21.
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRef
22.
Plantie, M., Crampes, M.: Survey on social community detection. In: Ramzan, N., van Zwol, R., Lee, J.-S., Clüver, K., Hua, X.-S. (eds.) Social Media Retrieval. CCN, pp. 65–85. Springer, London (2013)CrossRef
23.
Psorakis, I., Roberts, S., Ebden, M., Sheldon, B.: Overlapping community detection using bayesian non-negative matrix factorization. Phys. Rev. E 83(6), 066114 (2011)CrossRef
24.
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA 101(9), 2658–2663 (2004)CrossRef
25.
Schmidt, M.N., Laurberg, H.: Nonnegative matrix factorization with gaussian process priors. Comput. Intell. Neurosci. 2008, 3 (2008)CrossRef
26.
Shi, M., Yi, Q., Lv, J.: Symmetric nonnegative matrix factorization with beta-divergences. IEEE Signal Process. Lett. 19(8), 539–542 (2012)CrossRef
27.
Shi, X., Lu, H., He, Y., He, S.: Community detection in social network with pairwisely constrained symmetric non-negative matrix factorization. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, pp. 541–546. ACM, New York (2015)
28.
Tan, V.Y.F., Fevotte, C.: Automatic relevance determination in nonnegative matrix factorization with the beta-divergence. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1592–1605 (2013)CrossRef
29.
Tang, L., Liu, H.: Community detection and mining in social media. Synth. Lect. Data Min. Knowl. Discov. 2(1), 1–137 (2010)CrossRef
30.
Tang, X., Xu, T., Feng, X., Yang, G.: Uncovering community structures with initialized bayesian nonnegative matrix factorization. PLoS ONE 9(9), e107884 (2014)CrossRef
31.
Wang, D., Li, T., Zhu, S., Ding, C.: Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 307–314. ACM (2008)
32.
Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Discov. 22(3), 493–521 (2011)MathSciNetCrossRefMATH
33.
Wu, M., Scholkopf, B.: A local learning approach for clustering. Adv. Neural Inf. Process. Syst. 19, 1529 (2007)
34.
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43:1–43:35 (2013)CrossRefMATH
35.
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 267–273. ACM (2003)
36.
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp. 3:1–3:8 (2012)
37.
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 587–596. ACM (2013)
38.
Zhao, Y., Levina, E., Zhu, J.: Community extraction for social networks. Proc. Natl. Acad. Sci. 108(18), 7321–7326 (2011)CrossRef
Metadaten
Titel
Community Inference with Bayesian Non-negative Matrix Factorization
verfasst von
Xiaohua Shi
Hongtao Lu
Copyright-Jahr
2016
Buch
Web Technologies and Applications

Print ISBN: 978-3-319-45813-7

Electronic ISBN: 978-3-319-45814-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-45814-4_17