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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2016

01.12.2016 | Original Article

An empirical comparison of influence measurements for social network analysis

verfasst von: Khaled Almgren, Jeongkyu Lee

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2016

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Abstract

The studying of social influence can be used to understand and solve many complicated problems in social network analysis such as predicting influential users. This paper focuses on the problem of predicting influential users on social networks. We introduce a three-level hierarchy that classifies the influence measurements. The hierarchy categorizes the influence measurements by three folds, i.e., models, types and algorithms. Using this hierarchy, we classify the existing influence measurements. We further compare them based on an empirical analysis in terms of performance, accuracy and correlation using datasets from two different social networks to investigate the feasibility of influence measurements. Our results show that predicting influential users does not only depend on the influence measurements but also on the nature of social networks. Our goal is to introduce a standardized baseline for the problem of predicting influential users on social networks.
Vorheriger Artikel Influence of social relations on human mobility and sociality: a study of social ties in a cellular network
Nächster Artikel Optimising influence in social networks using bounded rationality models

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Literatur
Aggarwal CC (2011) An introduction to social network data analytics. Springer, New YorkCrossRefMATH
Almgren K, Lee J (2015) Who influences whom: content-based approach for predicting influential users in social networks. In: International conference on advances in big data analytics, pp 89–99
Almgren K, Lee J (2016) Applying an influence measurement framework to large social network. J Netw Technol 7(1):7
Anger I, Kittl C (2011) Measuring influence on twitter. In: Proceedings of the 11th international conference on knowledge management and knowledge technologies. ACM, p 31
Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 65–74
Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Springer, pp 1–4
Black PE (2004) Sparse graph. In: Dictionary of algorithms and data structures. National Institute of Standards and Technology
Bonacich P, Lloyd P (2001) Eigenvector-like measures of centrality for asymmetric relations. Soc Netw 23(3):191–201CrossRef
Brandes U (2001) A faster algorithm for betweenness centrality*. J Math Sociol 25(2):163–177CrossRefMATH
Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter: the million follower fallacy. ICWSM 10(10–17):30
Ellison NB et al (2007) Social network sites: definition, history, and scholarship. J Comput Mediat Commun 13(1):210–230MathSciNetCrossRef
Etherington D (2014) Flickr at 10: 1m photos shared per day, 170 % increase since making 1tb free. February 10:25–45
Granovetter MS (1973) The strength of weak ties. Am J Sociol 78:1360–1380CrossRef
Hogg T, Lerman K (2012) Social dynamics of Digg. EPJ Data Sci 1(1):1–26CrossRef
Katz E, Lazarsfeld PF (1955) Personal influence. The part played by people in the flow of mass communications. Transaction Publishers, New Brunswick
Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 137–146
Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web. ACM, pp 591–600
Leavitt A, Buchard E, Fischer D, Gilbert S (2009) The influentials: new approaches for analyzing influence on twitter. Web Ecol Project 4:1–18
Lee C, Kwak H, Park H, Moon S (2010) Finding influentials based on the temporal order of information adoption in twitter. In: Proceedings of the 19th international conference on World wide web. ACM, pp 1137–1138
Li H, Cui JT, Ma JF (2015) Social influence study in online networks: a three-level review. J Comput Sci Technol 30(1):184–199MathSciNetCrossRef
Li J, Peng W, Li T, Sun T (2013a) Social network user influence dynamics prediction. In: Web technologies and applications. Springer, pp 310–322
Li X, Cheng S, Chen W, Jiang F (2013b) Novel user influence measurement based on user interaction in microblog. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, pp 615–619
Liao Q, Wang W, Han Y, Zhang Q (2013) Analyzing the influential people in sina weibo dataset. In: Global communications conference (GLOBECOM), 2013 IEEE. IEEE, pp 3066–3071
Lü L, Zhang YC, Yeung CH, Zhou T (2011) Leaders in social networks, the delicious case. PloS One 6(6):e21,202CrossRef
Maharani W, Gozali AA, et al (2014) Degree centrality and eigenvector centrality in twitter. In: Telecommunication systems services and applications (TSSA), 2014 8th international conference on. IEEE, pp 1–5
McConnell RM, Spinrad JP (1994) Linear-time modular decomposition and efficient transitive orientation of comparability graphs. SODA 94:536–545MathSciNetMATH
Newman ME (2001) Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys Rev E 64(1):016,132CrossRef
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830MathSciNetMATH
Pempek TA, Yermolayeva YA, Calvert SL (2009) College students’ social networking experiences on facebook. J Appl Dev Psychol 30(3):227–238CrossRef
Probst F, Grosswiele DKL, Pfleger DKR (2013) Who will lead and who will follow: identifying influential users in online social networks. Bus Inf Syst Eng 5(3):179–193CrossRef
Rabade R, Mishra N, Sharma S (2014) Survey of influential user identification techniques in online social networks. In: Recent advances in intelligent informatics. Springer, pp 359–370
Reilly CF, Salinas D, De Leon D (2014) Ranking users based on influence in a directional social network. In: Computational science and computational intelligence (CSCI), 2014 international conference on, IEEE, vol 2, pp 237–240
Robertson S (2000) Evaluation in information retrieval. In: Lectures on information retrieval. Springer, pp 81–92
Sang J, Xu C (2013) Social influence analysis and application on multimedia sharing websites. ACM Trans Multimed Comput Commun Appl (TOMM) 9(1s):53MathSciNet
Scoot J (1992) Social network analysis theory and application. Sage, Newberry Park, CA
Singh S, Mishra N, Sharma S (2013) Survey of various techniques for determining influential users in social networks. In: Emerging trends in computing, communication and nanotechnology (ICE-CCN), 2013 international conference on, IEEE, pp 398–403
Stelzner MA (2011) Social media marketing industry report. Soc Media Exam 41:1–12
Sun B, Ng VT (2013) Identifying influential users by their postings in social networks. Springer, BerlinCrossRef
Sun C, Zhang L, Li Q (2013) Who are influentials on micro-blogging services: evidence from social network analysis. In: PACIS, p 25
Tang L, Liu H (2010) Community detection and mining in social media. Synth Lect Data Min Knowl Discov 2(1):1–137CrossRef
Wang C, Chen W, Wang Y (2012) Scalable influence maximization for independent cascade model in large-scale social networks. Data Min Knowl Discov 25(3):545–576MathSciNetCrossRefMATH
Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consum Res 34(4):441–458CrossRef
Weng J, Lim EP, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on Web search and data mining. ACM, pp 261–270
Yi X, Han Y, Wang X (2013) The evaluation of online social networks nodes influence based on users attribute and behavior. In: Frontiers in internet technologies. Springer, pp 9–20
Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press, CambridgeCrossRef
Zhang Y, Mo J, He T (2012) User influence analysis on micro blog. In: Cloud computing and intelligent systems (CCIS), 2012 IEEE 2nd international conference on, IEEE, vol 3, pp 1474–1478
Metadaten
Titel
An empirical comparison of influence measurements for social network analysis
verfasst von
Khaled Almgren
Jeongkyu Lee
Publikationsdatum
01.12.2016
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2016
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0360-y

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