Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Reducing Costs in HSM-Based Data Centers Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Influence Maximization in Social Media Networks Using Hypergraphs

Authors : Flora Amato, Vincenzo Moscato, Antonio Picariello, Giancarlo Sperlí

Published in: Green, Pervasive, and Cloud Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, inspired by hypergraph-based approaches, we propose a novel data model for social media networks: it allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications. From the other hand, we have tested several influence maximization algorithms leveraging the introduced network structure in order to show the advantages to consider also “user-to-multimedia” relationships (in addition to the “user-to-user” ones) in the influence analysis problem. Preliminary experiments using data of several social media networks shows how our approach obtains very promising results.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Energy-Efficient Dynamic Consolidation of Virtual Machines in Big Data Centers
next chapter A Modified Segmentation Approach for Overlapping Elliptical Objects with Various Sizes
Footnotes
Literature
1.
Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. J. Consum. Res. 34(4), 441–458 (2007)CrossRef
2.
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816. ACM (2009)
3.
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. In: ICWSM, vol. 10, issue (10–17), p. 30 (2010)
4.
Di Lorenzo, G., Moscato, F., Mazzocca, N., Vittorini, V.: Automatic analysis of control flow in web services composition processes. In: 2007 15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing, PDP 2007, pp. 299–306. IEEE (2007)
5.
Di Lorenzo, G., Mazzocca, N., Moscato, F., Vittorini, V.: Towards semantics driven generation of executable web services compositions. JSW 2(5), 1–15 (2007)CrossRef
6.
Amato, F., Mazzeo, A., Penta, A., Picariello, A.: Using NLP and ontologies for notary document management systems. In: 2008 19th International Workshop on Database and Expert Systems Application, DEXA 2008, pp. 67–71. IEEE (2008)
7.
Amato, F., Mazzeo, A., Moscato, V., Picariello, A.: A framework for semantic interoperability over the cloud. In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 1259–1264. IEEE (2013)
8.
Sperlì, G., Amato, F., Moscato, V., Picariello, A.: Multimedia social network modeling using hypergraphs. Int. J. Multimedia Data Eng. Manage. (IJMDEM) 7(3), 53–77 (2016)CrossRef
9.
Palmieri, F.: Network-aware scheduling for real-time execution support in data-intensive optical grids. Future Gener. Comput. Syst. 25(7), 794–803 (2009)CrossRef
10.
Jin, X., Luo, J., Yu, J., Wang, G., Joshi, D., Han, J.: Reinforced similarity integration in image-rich information networks. IEEE Trans. Knowl. Data Eng. 25(2), 448–460 (2013)CrossRef
11.
Palmieri, F.: Bayesian resource discovery in infrastructure-less networks. Inf. Sci. 376, 95–109 (2017)CrossRef
12.
Palmieri, F.: Scalable service discovery in ubiquitous and pervasive computing architectures: A percolation-driven approach. Future Gener. Comput. Syst. 29(3), 693–703 (2013)CrossRef
13.
Zhu, Z., Su, J., Kong, L.: Measuring influence in online social network based on the user-content bipartite graph. Comput. Hum. Behav. 52, 184–189 (2015)CrossRef
14.
Gao, J., et al.: A graph-based consensus maximization approach for combining multiple supervised and unsupervised models. IEEE Trans. Knowl. Data Eng. 25(1), 15–28 (2013)CrossRef
15.
Qi, G.-J., Aggarwal, C.C., Huang, T.S.: On clustering heterogeneous social media objects with outlier links. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. pp. 553–562. ACM (2012)
16.
Chen, B., Wang, J., Huang, Q., Mei, T.: Personalized video recommendation through tripartite graph propagation. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. pp. 1133–1136. ACM (2012)
17.
Carullo, G., Castiglione, A., De Santis, A., Palmieri, F.: A triadic closure and homophily-based recommendation system for online social networks. World Wide Web 18(6), 1579–1601 (2015)CrossRef
18.
Castiglione, A., Cattaneo, G., De Santis, A.: A forensic analysis of images on online social networks. In: 2011 Third International Conference on Intelligent Networking and Collaborative Systems, pp. 679–684, November 2011
19.
Castiglione, A., D’Alessio, B., De Santis, A.: Steganography and secure communication on online social networks and online photo sharing. In: 2011 International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 363–368, October 2011
20.
Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., He, X.: Music recommendation by unified hypergraph: Combining social media information and music content. In: Proceedings of the International Conference on Multimedia, pp. 391–400. ACM (2010)
21.
Anandkumar, A., Sedghi, H.: Learning mixed membership community models in social tagging networks through tensor methods (2015). arXiv preprint arXiv:​1503.​04567
22.
Colace, F., De Santo, M., Greco, L., Moscato, V., Picariello, A.: A collaborative user-centered framework for recommending items in online social networks. Comput. Hum. Behav. 51, 694–704 (2015)CrossRef
23.
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM (2001)
24.
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
25.
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 420–1443 (1978)
26.
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)CrossRef
27.
Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 199–208. ACM (2010)
28.
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. Theory Comput. 11(4), 105–147 (2015)MathSciNetCrossRefMATH
29.
Amato, F., Moscato, V., Picariello, A., Sperlí, G.: Multimedia social network modeling: A proposal. In: 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), pp. 448–453. IEEE (2016)
30.
Fang, Q., Sang, J., Xu, C., Rui, Y.: Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning. IEEE Trans. Multimedia 16(3), 796–812 (2014)CrossRef
31.
Colace, F., De Santo, M., Greco, L., Amato, F., Moscato, V., Picariello, A.: Terminological ontology learning and population using latent dirichlet allocation. J. Vis. Lang. Comput. 25(6), 818–826 (2014)CrossRef
32.
Amato, F., Barbareschi, M., Casola, V., Mazzeo, A.: An FPGA-based smart classifier for decision support systems. In: Zavoral, F., Jung, J.J., Badica, C. (eds.) Intelligent Distributed Computing VII. Studies in Computational Intelligence, vol. 511, pp. 289–299. Springer, Heidelberg (2014)CrossRef
33.
Colace, F., De Santo, M., Greco, L.: A probabilistic approach to tweets’ sentiment classification. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), pp. 37–42. IEEE (2013)
34.
Heintz, B., Chandra, A.: Beyond graphs: Toward scalable hypergraph analysis systems. ACM SIGMETRICS Perform. Eval. Rev. 41(4), 94–97 (2014)CrossRef
35.
Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: Clustering, classification, and embedding. In: NIPS, vol. 19, pp. 1633–1640 (2006)
36.
Tang, Y., et al.: Influence maximization: Near-optimal time complexity meets practical efficiency. In: SIGMOD 2014 Proceedings of 2014 ACM International Conference on Management of Data, pp. 75–86. ACM, New York (2014)
37.
Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: A martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, pp. 1539–1554. ACM, New York (2015)
38.
39.
Miller, G.A.: Wordnet: A lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRef
40.
Boccignone, G., Chianese, A., Moscato, V., Picariello, A.: Context-sensitive queries for image retrieval in digital libraries. J. Intell. Inf. Syst. 31(1), 53–84 (2008)CrossRef
Metadata
Title
Influence Maximization in Social Media Networks Using Hypergraphs
Authors
Flora Amato
Vincenzo Moscato
Antonio Picariello
Giancarlo Sperlí
Copyright Year
2017
Book
Green, Pervasive, and Cloud Computing

Print ISBN: 978-3-319-57185-0

Electronic ISBN: 978-3-319-57186-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-57186-7_17

Premium Partner

    Image Credits