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
Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 3/2013

01.09.2013 | Original Article

User similarities on social networks

verfasst von: Cuneyt Gurcan Akcora, Barbara Carminati, Elena Ferrari

Erschienen in: Social Network Analysis and Mining | Ausgabe 3/2013

Einloggen

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

search-config
loading …

Abstract

A key problem in online social networks is the identification of user characteristics and the analysis of how these are reflected in the graph structure evolution. The basis to tackle this issue is user similarity measures. In this paper, we propose a novel user similarity measure for online social networks, which combines both network and profile similarity. Since user profile data could be missing proposed measure is complemented by a technique to infer missing items from profile of the user’s contacts. The second main contribution of this paper is an extensive performance evaluation of the proposed measures with respect to some of the most relevant measures already proposed in the literature. The performance evaluation study has been conducted on a variety of data sets (i.e., Facebook, Youtube, Epinions and DBLP data sets) to see how different scenarios and graph characteristics affect the measures’ performance.
Vorheriger Artikel Understanding organ transplantation in the USA using geographical social networks
Nächster Artikel Employing online social networks to monitor and evaluate training of digital inclusion agents

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 "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

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
Fußnoten
1
User inputs with Facebook auto-completion and aggregation was imposed just in recent years. Before this, users could enter unstructured texts.
 
2
where |Sb| = 1 in case of non-structured items, like gender.
 
3
This is done by creating the graph using only edges established before time T.
 
4
In the table, precision is the correct inferrals over all inferrals, i.e., precision = #correct inferrals/#all inferrals.
 
6
If there is no profile information, a generic list of celebrity accounts are offered.
 
7
Related videos list of a video can not be determined by the user who uploaded the video: http://​support.​google.​com/​youtube/​bin/​answer.​py?​hl=​en&​answer=​92651
 
8
In Graph theory, triadic closure is used to refer to predictions for such graphs where two pairs of nodes have strong ties, and a weak tie among them is expected, i.e., the dashed line already exists or it is expected to be formed in future. Our experiments try to predict this edge.
 
9
Values are rounded to two decimal points.
 
10
This problem is known as the cold start problem in recommender systems.
 
Literatur
Adamic L, Buyukkokten O, Adar E (2003) A social network caught in the web. First Monday 8(6):6CrossRef
Akcora C, Carminati B, Ferrari E (2011) Network and profile based measures for user similarities on social networks. In: IEEE international conference on information reuse and integration (IRI), IEEE, pp 292–298
Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on Web search and data mining, ACM, pp 635–644
Bhattacharyya P, Garg A, Wu S (2010) Analysis of user keyword similarity in online social networks. Soc Netw Anal Min 1:1–16
Boriah S, Chandola V, Kumar V (2008) Similarity measures for categorical data: a comparative evaluation. Redox Rep 30(2):3
Bringmann B, Berlingerio M, Bonchi F, Gionis A (2010) Learning and predicting the evolution of social networks. Intell Syst IEEE 25(4):26–35CrossRef
Cheng X, Liu J (2009) Nettube: exploring social networks for peer-to-peer short video sharing. In: INFOCOM 2009, IEEE, IEEE, pp 1152–1160
Cheng X, Dale C, Liu J (2008) Statistics and social network of youtube videos. In: Quality of service, 2008. IWQoS 2008. 16th International Workshop on, IEEE, pp 229–238
Cover TM, Thomas JA (1991) Elements of information theory. Wiley-Interscience, New York
Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22:143–177CrossRef
Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. In: Proceedings of the 13th international conference on World Wide Web, ACM, New York, NY, USA, WWW ’04, pp 403–412. doi:10.​1145/​988672.​988727
Krishnamurthy B, Wills C (2010) On the leakage of personally identifiable information via online social networks. ACM SIGCOMM 40(1):112–117CrossRef
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inform Sci Technol 58(7):1019–1031CrossRef
Lindamood J, Heatherly R, Kantarcioglu M, Thuraisingham B (2009) Inferring private information using social network data. In: Proceedings of the 18th WWW, ACM, pp 1145–1146
Massa P, Avesani P (2004) Trust-aware bootstrapping of recommender systems. In: Proceedings of ECAI 2006: workshop on recommender systems, Citeseer, pp 29–33
McPherson M, Smith-Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415–444CrossRef
Mislove A, Viswanath B, Gummadi K, Druschel P (2010) You are who you know: inferring user profiles in online social networks. In: Proceedings of the third WSDM conference, ACM, pp 251–260
Mueller D (1976) Public choice: a survey. J Econ Literature 14(2):395–433
Newman M, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026,113CrossRef
Penrose L (1946) The elementary statistics of majority voting. J Roy Stat Soc B 109(1):53–57CrossRef
Spertus E, Sahami M, Buyukkokten O (2005) Evaluating similarity measures: a large-scale study in the orkut social network. In: Proceedings of the 11th SIGKDD, ACM, pp 678–684
Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM SIGCOMM workshop on social networks (WOSN’09)
Zhou R, Khemmarat S, Gao L (2010) The impact of youtube recommendation system on video views. In: Proceedings of the 10th annual conference on Internet measurement, ACM, pp 404–410
Zhou Y, Cheng H, Yu J (2009) Graph clustering based on structural/attribute similarities. Proc VLDB Endowment 2(1):718–729
Metadaten
Titel
User similarities on social networks
verfasst von
Cuneyt Gurcan Akcora
Barbara Carminati
Elena Ferrari
Publikationsdatum
01.09.2013
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 3/2013
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-012-0090-8

Weitere Artikel der Ausgabe 3/2013

Social Network Analysis and Mining 3/2013 Zur Ausgabe

Original Article

A system for ranking organizations using social scale analysis

Original Article

A framework for mobile and context-aware applications applied to vehicular social networks

Original Article

A social network approach to change detection in the interdependence structure of global stock markets

Original Article

Model for generating artificial social networks having community structures with small-world and scale-free properties

Original Article

Social network user lifetime

Original Article

Molecular model of dynamic social network based on e-mail communication