Abstract
Online social networks (OSN) have today reached a remarkable capillary diffusion. There are numerous examples of very large platforms people use to communicate and maintain relationships. People also subscribe to several OSNs, e.g., people create accounts on Facebook, Twitter, and so on. This phenomenon leads to online social internetworking (OSI) scenarios where users who subscribe to multiple OSNs are termed as bridges. Unfortunately, several important features make the study of information propagation in an OSI scenario a difficult task, e.g., correlations in both the structural characteristics of OSNs and the bridge interconnections among them, heterogeneity and size of OSNs, activity factors, cross-posting propensity, and so on. In this article, we propose a directed random graph-based model that is amenable to efficient numerical solution to analyze the phenomenon of information propagation in an OSI scenario; in the model development, we take into account heterogeneity and correlations introduced by both topological (correlations among nodes degrees and among bridge distributions) and user-related factors (activity index, cross-posting propensity). We first validate the model predictions against simulations on snapshots of interconnected OSNs in a reference scenario. Subsequently, we exploit the model to show the impact on the information propagation of several characteristics of the reference scenario, i.e., size and complexity of the OSI scenario, degree distribution and overall number of bridges, growth and decline of OSNs in time, and time-varying cross-posting users propensity.
- Yong-Yeol Ahn, Seungyeop Han, Haewoon Kwak, Sue Moon, and Hawoong Jeong. 2007. Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th International Conference on World Wide Web (WWW’07). ACM, New York, NY, 835–844. Google Scholar
Digital Library
- Konstantin Avrachenkov, Koen De Turck, Dieter Fiems, and Balakrishna Prabhu. Information dissemination processes in directed social networks. In Proceedings of the International Workshop on Modeling, Analysis, and Management of Social Networks and their Applications (SOCNET’14).Google Scholar
- Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. 2006. Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06). ACM, New York, NY, 44--54. Google Scholar
Digital Library
- Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. The role of social networks in information diffusion. In Proceedings of the 21st International Conference on World Wide Web (WWW’12). Google Scholar
Digital Library
- Francesco Buccafurri, Vincenzo Daniele Foti, Gianluca Lax, Antonino Nocera, and Domenico Ursino. 2013. Bridge analysis in a social internetworking scenario. Inf. Sci. 224, 0 (2013), 1--18. Google Scholar
Digital Library
- Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna Gummadi. 2010. Measuring user influence in twitter: The million follower fallacy. In Proceedings of the International AAAI Conference on Web and Social Media.Google Scholar
- Meeyoung Cha, Alan Mislove, Ben Adams, and Krishna P. Gummadi. 2008. Characterizing social cascades in flickr. In Proceedings of the 1st Workshop on Online Social Networks. ACM, 13--18. Google Scholar
Digital Library
- Meeyoung Cha, Alan Mislove, and Krishna P. Gummadi. A measurement-driven analysis of information propagation in the flickr social network. In Proceedings of the ACM International Conference on World Wide Web (WWW’09). Google Scholar
Digital Library
- Giorgio Fagiolo. 2007. Clustering in complex directed networks. Physical Review E 76 (2007), 026107. Issue 2.Google Scholar
Cross Ref
- Ali Faqeeh, Sergey Melnik, and James P. Gleeson. 2015. Network cloning unfolds the effect of clustering on dynamical processes. Phys. Rev. E 91, 5 (2015), 052807-1--052807-10.Google Scholar
Cross Ref
- M. Fire, R. Tenenboim, R. Puzis, O. Lesser, L. Rokach, and Y. Elovici. 2013. Computationally efficient link prediction in variety of social networks. ACM Trans. Intell. Syst. Technol. 5, 1 (2013), 10. Google Scholar
Digital Library
- Roberto González, Rubén Cuevas, Ángel Cuevas, Reza Farahbakhsh, Reza Motamedi, and Reza Rejaie. Characterization of information propagation in google+ and its comparison with twitter. Technical Report CIS-TR-10-13, October 2013. http://mirage.cs.uoregon.edu/pub/Ripples-2.pdf.Google Scholar
- Roberto Gonzalez, Ruben Cuevas, Reza Motamedi, Reza Rejaie, and Angel Cuevas. Google+ or google-?: Dissecting the evolution of the new OSN in its first year. In Proceedings of the ACM International Conference on World Wide Web (WWW’13). Google Scholar
Digital Library
- Adrien Guille, Hakim Hacid, Cecile Favre, and Djamel A. Zighed. 2013. Information diffusion in online social networks: A survey. SIGMOD Rec. 42, 2 (2013), 17--28. Google Scholar
Digital Library
- Changjun Hu, Wenwen Xu, and Peng Shi. 2015. Information diffusion in online social networks: Models, methods and applications. In Web-Age Information Management, Xiaokui Xiao and Zhenjie Zhang (Eds.). Lecture Notes in Computer Science, Vol. 9391. Springer International Publishing, 65--76.Google Scholar
- Balachander Krishnamurthy, Phillipa Gill, and Martin Arlitt. 2008. A few chirps about twitter. In Proceedings of the First Workshop on Online Social Networks. ACM, 19--24. Google Scholar
Digital Library
- Ravi Kumar, Jasmine Novak, and Andrew Tomkins. 2006. Structure and evolution of online social networks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06). Google Scholar
Digital Library
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is twitter, a social network or a news media? In Proceedings of the 19th ACM International Conference on World Wide Web (WWW’10). Google Scholar
Digital Library
- Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, and Michael W. Mahoney. 2009. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6, 1 (2009), 29--123.Google Scholar
Cross Ref
- Weihua Li, Shaoting Tang, Wenyi Fang, Quantong Guo, Xiao Zhang, and Zhiming Zheng. 2015. How multiple social networks affect user awareness: The information diffusion process in multiplex networks. Phys. Rev. E 92 (Oct. 2015), 042810. Issue 4.Google Scholar
- Han Liu, Atif Nazir, Jinoo Joung, and Chen-Nee Chuah. 2013. Modeling/predicting the evolution trend of osn-based applications. In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 771--780. Google Scholar
Digital Library
- Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, and Shiqiang Yang. Mining topic-level influence in heterogeneous networks. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). Google Scholar
Digital Library
- Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC’07). Google Scholar
Digital Library
- M. E. J. Newman, S. H. Strogatz, and D. J. Watts. 2001. Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64 (Jul. 2001), 026118. Issue 2.Google Scholar
Cross Ref
- R. Ottoni, D. Las Casas, J. P. Pesce, W. Meira Jr., C. Wilson, A. Mislove, and V. Almeida. Of pins and tweets: Investigating how users behave across image-and text-based social networks. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM'14). 386--395.Google Scholar
- Reza Rejaie, Mojtaba Torkjazi, Masoud Valafar, and Walter Willinger. 2010. Sizing up online social networks. IEEE Netw. 24, 5 (2010), 32--37. Google Scholar
Digital Library
- Angel Cuevas Reza Farahbakhsh and Noel Crespi. 2015. Characterization of cross-posting activity for professional users across major OSNs. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’15). Google Scholar
Digital Library
- Bruno Ribeiro. 2014. Modeling and predicting the growth and death of membership-based websites. In Proceedings of the 23rd International Conference on World Wide Web. 653--664. Google Scholar
Digital Library
- M. Salehi, R. Sharma, M. Marzolla, M. Magnani, P. Siyari, and D. Montesi. 2015. Spreading processes in multilayer networks. IEEE Trans. Netw. Sci. Eng. 2, 2 (2015), 65--83.Google Scholar
Cross Ref
- W. C. Su. 2014. Integrating and mining virtual communities across multiple online social networks: Concepts, approaches and challenges. In Proceedings of the 4th International Conference on Digital Information and Communication Technology and its Applications (DICTAP’14). 199--204.Google Scholar
Cross Ref
- Mojtaba Torkjazi, Reza Rejaie, and Walter Willinger. 2009. Hot today, gone tomorrow: On the migration of MySpace users. In Proceedings of the 2nd ACM Workshop on Online Social Networks. ACM, 43--48. Google Scholar
Digital Library
- S. Wen, M. S. Haghighi, C. Chen, Y. Xiang, W. Zhou, and W. Jia. 2015. A sword with two edges: Propagation studies on both positive and negative information in online social networks. IEEE Trans. Comput. 64, 3 (2015), 640--653.Google Scholar
Digital Library
- O. Yagan, Dajun Qian, Junshan Zhang, and D. Cochran. 2013. Conjoining speeds up information diffusion in overlaying social-physical networks. IEEE J. Select. Areas Commun. 31, 6 (2013), 1038--1048.Google Scholar
Cross Ref
- Jaewon Yang and Jure Leskovec. Modeling information diffusion in implicit networks. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM’10). Google Scholar
Digital Library
- Shaozhi Ye and S. Felix Wu. 2010. Measuring message propagation and social influence on twitter.com. In Social Informatics. LNCS, Vol. 6430. Google Scholar
Digital Library
- Jichang Zhao, Junjie Wu, Xu Feng, Hui Xiong, and Ke Xu. 2012. Information propagation in online social networks: A tie-strength perspective. Knowl. Inf. Syst. 32, 3 (2012), 589--608. Google Scholar
Digital Library
Index Terms
A Model of Information Diffusion in Interconnected Online Social Networks
Recommendations
Ego network structure in online social networks and its impact on information diffusion
In the last few years, Online Social Networks (OSNs) attracted the interest of a large number of researchers, thanks to their central role in the society. Through the analysis of OSNs, many social phenomena have been studied, such as the viral diffusion ...
An enriched social behavioural information diffusion model in social networks
Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur ...
Social exchange in online social networks. The reciprocity phenomenon on Facebook
Our research is focused on reciprocity, which is crucial for social exchanges.The online social network platform of our choice was Facebook, which is one of the most successful online social sites.In our study we found strong empirical evidence that an ...






Comments