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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2016 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Virinchi Srinivas, Pabitra Mitra

Erschienen in: Link Prediction in Social Networks

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Link prediction deals with predicting new links which are likely to emerge in network in the future, given the network at the current time. It has a wide range of applications including recommender systems, spam mail classification, identifying domain experts in various research areas, etc. In this chapter, we discuss the prior art in link prediction literature.

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
Nächstes Kapitel Link Prediction Using Thresholding Nodes Based on Their Degree
Fußnoten
1
Even though we use the term similarity measure, it is a similarity function and need not be a measure.
 
2
Owing to high computational overhead.
 
Literatur
1.
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2003)CrossRef
2.
Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’14, pp. 1266–1275. New York, NY, USA, 24–27 August 2014
3.
Benchettara, N., Kanawati, R., Rouveirol, C.: A supervised machine learning link prediction approach for academic collaboration recommendation. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pp. 253–256. Barcelona, Spain, 26–30 September 2010
4.
Bilgic, M., Namata, G., Getoor, L.: Combining collective classification and link prediction. In: Workshops Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pp. 381–386. Omaha, Nebraska, 28–31 October 2007
5.
Cao, B., Liu, N.N., Yang, Q.: Transfer learning for collective link prediction in multiple heterogenous domains. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 159–166. Haifa, Israel, 21–24 June 2010
6.
Chiang, K., Natarajan, N., Tewari, A., Dhillon, I.S.: Exploiting longer cycles for link prediction in signed networks. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, pp. 1157–1162. Glasgow, United Kingdom, 24–28 October 2011
7.
Cohen, S., Zohar, A.: An axiomatic approach to link prediction. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 58–64. Austin, Texas, USA, 25–30 January 2015
8.
Davis, D.A., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks. In: International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 281–288. Kaohsiung, Taiwan, 25–27 July 2011
9.
De, A., Ganguly, N., Chakrabarti, S.: Discriminative link prediction using local links, node features and community structure. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1009–1018. Dallas, TX, USA, 7–10 December 2013
10.
Dong, Y., Tang, J., Wu, S., Tian, J., Chawla, N.V., Rao, J., Cao, H.: Link prediction and recommendation across heterogeneous social networks. In: 12th IEEE International Conference on Data Mining, ICDM 2012, pp. 181–190. Brussels, Belgium,10–13 December 2012
11.
Gao, S., Denoyer, L., Gallinari, P.: Link prediction via latent factor blockmodel. In: Proceedings of the 21st World Wide Web Conference, WWW 2012 (Companion Volume), pp. 507–508. Lyon, France, 16–20 April 2012
12.
Ge, L., Zhang, A.: Pseudo cold start link prediction with multiple sources in social networks. In: Proceedings of the Twelfth SIAM International Conference on Data Mining, pp. 768–779. Anaheim, California, USA, 26–28 April 2012
13.
Kashima, H., Abe, N.: A parameterized probabilistic model of network evolution for supervised link prediction. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), pp. 340–349. Hong Kong, China, 18–22 December 2006
14.
Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., Tsuda, K.: Link propagation: A fast semi-supervised learning algorithm for link prediction. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2009, pp. 1100–1111. Sparks, Nevada, USA, 30 April–2 May 2009
15.
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefMATH
16.
Kim, J., Choy, M., Kim, D., Kang, U.: Link prediction based on generalized cluster information. In: 23rd International World Wide Web Conference, WWW’14 (Companion Volume), pp. 317–318. Seoul, Republic of Korea, 7–11 April 2014
17.
Kuo, T., Yan, R., Huang, Y., Kung, P., Lin, S.: Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 775–783. Chicago, IL, USA, 11–14 August 2013
18.
Lee, C., Pham, M., Kim, N., Jeong, M.K., Lin, D.K.J., Chaovalitwongse, W.A.: A novel link prediction approach for scale-free networks. In: 23rd International World Wide Web Conference, WWW’14 (Companion Volume), pp. 1333–1338. Seoul, Republic of Korea, 7–11 April 2014
19.
Leroy, V., Cambazoglu, B.B., Bonchi, F.: Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 393–402. ACM (2010)
20.
Li, X., Du, N., Li, H., Li, K., Gao, J., Zhang, A.: A deep learning approach to link prediction in dynamic networks. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 289–297. Philadelphia, Pennsylvania, USA, 24–26 April 2014
21.
Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: Proceedings of the 2003 ACM CIKM International Conference on Information and Knowledge Management, pp. 556–559. New Orleans, Louisiana, USA, 2–8 November 2003
22.
Lichtenwalter, R., Chawla, N.V.: Lpmade: Link prediction made easy. J. Mach. Learn. Res. 12, 2489–2492 (2011)MATH
23.
Lichtenwalter, R., Chawla, N.V.: Vertex collocation profiles: subgraph counting for link analysis and prediction. In: Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, pp. 1019–1028. Lyon, France, 16–20 April 2012
24.
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD’10, p. 243 (2010)
25.
Liu, F., Liu, B., Wang, X., Liu, M., Wang, B.: Features for link prediction in social networks: A comprehensive study. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, pp. 1706–1711. Seoul, Korea (South), 14–17 October 2012
26.
Lu, L., Zhou, T.: Role of weak ties in link prediction of complex networks. In: Proceeding of the ACM First International Workshop on Complex Networks Meet Information & Knowledge Management, CIKM-CNIKM 2009, pp. 55–58. Hong Kong, China, 6 November 2009
27.
Miller, K.T., Griffiths, T.L., Jordan, M.I.: Nonparametric latent feature models for link prediction. In: Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009, pp. 1276–1284. Vancouver, British Columbia, Canada, 7–10 December 2009
28.
Murata, T., Moriyasu, S.: Link prediction of social networks based on weighted proximity measures. In: 2007 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007, Main Conference Proceedings, pp. 85–88. Silicon Valley, CA, USA, 2–5 November 2007
29.
Murata, T., Moriyasu, S.: Link prediction based on structural properties of online social networks. New Gener. Comput. 26(3), 245–257 (2008)CrossRef
30.
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E, Stat. Nonlinear Soft Matter Phys. 64, 025102 (2001)CrossRef
31.
Newman, M.E.J.: Networks: An Introduction. Oxford University Press Inc, New York (2010)CrossRef
32.
Pan, J.Y., Yang, H.J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 653–658. ACM (2004)
33.
Pujari, M., Kanawati, R.: Supervised rank aggregation approach for link prediction in complex networks. In: Proceedings of the 21st World Wide Web Conference, WWW 2012 (Companion Volume), pp. 1189–1196. Lyon, France, 16–20 April 2012
34.
Qi, G., Aggarwal, C.C., Huang, T.S.: Link prediction across networks by biased cross-network sampling. In: 29th IEEE International Conference on Data Engineering, ICDE 2013, pp. 793–804. Brisbane, Australia, 8–12 April 2013
35.
Raymond, R., Kashima, H.: Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs. In: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Proceedings, Part III, pp. 131–147. Barcelona, Spain, 20–24 September 2010
36.
Rümmele, N., Ichise, R., Werthner, H.: Exploring supervised methods for temporal link prediction in heterogeneous social networks. In: Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015 (Companion Volume), pp. 1363–1368. Florence, Italy, 18–22 May 2015
37.
Saito, K., Nakano, R., Kimura, M.: Prediction of link attachments by estimating probabilities of information propagation. In: Knowledge-Based Intelligent Information and Engineering Systems, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings, Part III, pp. 235–242. Vietri sul Mare, Italy, 12–14 September 2007
38.
Sarkar, P., Chakrabarti, D., Jordan, M.I.: Nonparametric link prediction in dynamic networks. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, 26 June–1 July 2012
39.
Sarkar, P., Chakrabarti, D., Moore, A.W.: Theoretical justification of popular link prediction heuristics. In: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 2722–2727. Barcelona, Catalonia, Spain, 16–22 July 2011
40.
Sarukkai, R.: Link prediction and path analysis using markov chains. Comput. Netw. 33(1–6), 377–386 (2000)CrossRef
41.
Scholz, M.: Node similarity as a basic principle behind connectivity in complex networks (2010). ArXiv preprint arXiv:​1010.​0803
42.
Shin, D., Si, S., Dhillon, I.S.: Multi-scale link prediction. In: 21st ACM International Conference on Information and Knowledge Management, CIKM’12, pp. 215–224. Maui, HI, USA, 29 October–02 November 2012
43.
Soundarajan, S., Hopcroft, J.E.: Using community information to improve the precision of link prediction methods. In: Proceedings of the 21st World Wide Web Conference, WWW 2012 (Companion Volume), pp. 607–608. Lyon, France, 16–20 April 2012
44.
Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pp. 183–190. Barcelona, Spain, 26–30 September 2010
45.
Tasnádi, E., Berend, G.: Supervised prediction of social network links using implicit sources of information. In: Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015 (Companion Volume), pp. 1117–1122. Florence, Italy, 18–22 May 2015
46.
Tylenda, T., Angelova, R., Bedathur, S.J.: Towards time-aware link prediction in evolving social networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis, SNAKDD 2009, p. 9. Paris, France, 28 June 2009
47.
Virinchi, S., Mitra, P.: Link prediction using power law clique distribution and common edges distribution. In: Proceedings of Pattern Recognition and Machine Intelligence—5th International Conference, PReMI 2013, pp. 739–744. Kolkata, India, 10–14 December 2013
48.
Virinchi, S., Mitra, P.: Similarity measures for link prediction using power law degree distribution. In: Proceedings of Neural Information Processing—20th International Conference, ICONIP 2013, Part II, pp. 257–264. Daegu, Korea, 3–7 November 2013
49.
Virinchi, S., Mitra, P.: Two-phase approach to link prediction. In: Proceedings of Neural Information Processing—21st International Conference, ICONIP 2014, Part II, pp. 413–420. Kuching, Malaysia, 3–6 November 2014
50.
Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pp. 322–331. Omaha, Nebraska, USA, 28–31 October 2007
51.
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)
52.
Zhu, J.: Using markov chains for structural link prediction in adaptive web sites. In: Proceedings of User Modeling 2001, 8th International Conference, UM 2001, pp. 298–302. Sonthofen, Germany, 13–17 July 2001
53.
Zhu, J., Hong, J., Hughes, J.G.: Using markov chains for link prediction in adaptive web sites. In: Proceedings of Soft-Ware 2002: Computing in an Imperfect World, First International Conference, Soft-Ware 2002, pp. 60–73, Belfast, Northern Ireland, 8–10 April 2002
54.
Zhu, J., Hong, J., Hughes, J.G.: Using markov models for web site link prediction. In: Proceedings of the 13th ACM Conference on Hypertext and Hypermedia, HYPERTEXT 2002, pp. 169–170. University of Maryland, College Park, MD, USA, 11–15 June 2002
Metadaten
Titel
Introduction
verfasst von
Virinchi Srinivas
Pabitra Mitra
Copyright-Jahr
2016
Buch
Link Prediction in Social Networks

Print ISBN: 978-3-319-28921-2

Electronic ISBN: 978-3-319-28922-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-28922-9_1

Premium Partner

    Bildnachweise