Abstract
Nodes in complex networks inherently represent different kinds of functional or organizational roles. In the dynamic process of an information cascade, users play different roles in spreading the information: some act as seeds to initiate the process, some limit the propagation and others are in-between. Understanding the roles of users is crucial in modeling the cascades. Previous research mainly focuses on modeling users behavior based upon the dynamic exchange of information with neighbors. We argue however that the structural patterns in the neighborhood of nodes may already contain enough information to infer users’ roles, independently from the information flow in itself. To approach this possibility, we examine how network characteristics of users affect their actions in the cascade. We also advocate that temporal information is very important. With this in mind, we propose an unsupervised methodology based on ensemble clustering to classify users into their social roles in a network, using not only their current topological positions, but also considering their history over time. Our experiments on two social networks, Flickr and Digg, show that topological metrics indeed possess discriminatory power and that different structural patterns correspond to different parts in the process. We observe that user commitment in the neighborhood affects considerably the influence score of users. In addition, we discover that the cohesion of neighborhood is important in the blocking behavior of users. With this we can construct topological fingerprints that can help us in identifying social roles, based solely on structural social ties, and independently from nodes activity and how information flows.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The p values are obtained from a one-tailed paired t test.
Fig. 11 
The classification accuracy for Digg dataset over 10 different test set (Color figure online)
References
Adamic L, Adar E (2005) How to search a social network. Soc Netw 27(3):187–203
Agarwal N, Liu H, Tang L, Yu PS (2008) Identifying the influential bloggers in a community. In: Proceedings of ACM International Conference on Web Search and Data Mining
Akaike H (1998) Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike. Springer, New York
Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of ACM International Conference on Web Search and Data Mining
Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Netw 29(4):555–564
Cha M, Benevenuto F, Ahn Y-Y, Gummadi KP (2012) Delayed information cascades in flickr: measurement, analysis, and modeling. Comput Netw 56(3):1066–1076
Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter: the million follower fallacy. In: Proceedings of AAAI International Conference on Weblogs and Social Media, vol. 10
Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the Flickr social network. In: Proceedings of ACM International Conference on World Wide Web
Choobdar S, Ribeiro P, Silva F (2012) Event detection in evolving networks. In: Proceedings of IEEE International Conference on Computational Aspects of Social Networks (CASoN), São Carlos, Brazil
Choobdar S, Silva F, Ribeiro P (2011) Network node label acquisition and tracking. In: Proceedings of Portuguese Conference on Artificial Intelligence, Progress in Artificial Intelligence
Cormode G, Shkapenyuk V, Srivastava D, Xu B (2009) Forward decay: a practical time decay model for streaming systems. In: Proceedings of IEEE International Conference on Data Engineering
Costa L, Rodrigues F, Hilgetag C, Kaiser M (2009) Beyond the average: detecting global singular nodes from local features in complex networks. Europhys Lett 87(1):18008
Danilevsky M, Wang C, Desai N, Han J (2013) Entity role discovery in hierarchical topical communities. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Easley D, Kleinberg J (2010) Networks, crowds, and markets, vol 6(1). Cambridge University Press, New York
Easley D, Kleinberg J (2012) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge
Fred A, Jain A (2002) Data clustering using evidence accumulation. In: Proceedings of International Conference on Pattern Recognition, vol. 4. Quebec City, Canada
Gallagher B, Eliassi-Rad T (2010) Leveraging label-independent features for classification in sparsely labeled networks: an empirical study. In: Proceedings of International Conference on Advances in Social Network Mining and Analysis. Berlin, Germany
Ghosh R, Lerman K (2010) Predicting influential users in online social networks. In: Proceedings of KDD Workshop on Social Network Analysis (SNA-KDD), July 2010
Ghosh R, Lerman K (2012) Rethinking centrality: the role of dynamical processes in social network analysis. arXiv preprint arXiv:1209.4616
Gionis A, Mannila H, Tsaparas P (2005) Clustering aggregation. In: Proceedings of IEEE International Conference on Data Engineering
Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of ACM International Conference on Web Search and Data Mining
Granovetter M (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380
Granovetter M (1985) Economic action and social structure: the problem of embeddedness. Am J Sociol 91:481–510
Guo S, Wang M, Leskovec J (2011) The role of social networks in online shopping: information passing, price of trust, and consumer choice. In: Proceedings of the 12th ACM Conference on Electronic Commerce
Henderson K, Gallagher B, Eliassi-Rad T, Tong H, Basu S, Akoglu L, Koutra D, Faloutsos C, Li L, Matsubara Y, et al. (2012) Rolx: structural role extraction & mining in large graphs. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China
Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103(3):038702
Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 32(3):241–254
Karypis G, Aggarwal R, Kumar V, Shekhar S (1997) Multilevel hypergraph partitioning: application in vlsi domain. In: Proceedings of the 34th Annual Design Automation Conference, ACM
Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311(5757):88–90
Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of ACM International Conference on World Wide Web
Lancichinetti A, Fortunato S (2012) Consensus clustering in complex networks. Sci Rep 2:336
Lee C, Kwak H, Park H, Moon S (2010) Finding influentials based on the temporal order of information adoption in twitter. In Proceedings of ACM International Conference on World Wide Web
Lerman K, Ghosh R, Surachawala T (2012) Social contagion: an empirical study of information spread on digg and twitter follower graphs. arXiv preprint arXiv:1202.3162
Myers SA, Zhu C, Leskovec J (2012) Information diffusion and external influence in networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Rissanen J (1978) Modeling by shortest data description. Automatica 14(5):465–471
Romero DM, Galuba W, Asur S, Huberman BA (2011) Influence and passivity in social media. In: Proceedings of the ECML/PKDD
Rossi R, Gallagher B, Neville J, Henderson K (2012) Role-dynamics: fast mining of large dynamic networks. In: Proceedings of ACM International Conference on World Wide Web. Lyon, France
Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. Knowledge-based intelligent information and engineering systems. Springer, New York
Strehl A, Ghosh J (2003) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617
Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: Proceedings of Conference on Uncertainty in Artificial Intelligence. Alberta, Canada
Topchy A, Law M, Jain A, Fred A (2004) Analysis of consensus partition in cluster ensemble. In: Proceedings of IEEE International Conference on Data Mining. Brighton, UK
Ver Steeg G, Ghosh R, Lerman K (2011) What stops social epidemics? In: Proceedings of AAAI International Conference on Weblogs and Social Media
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Wang H, Fan W, Yu PS, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Wang T, Srivatsa M, Agrawal D, Liu L (2012) Microscopic social influence. In: Proceedings of SIAM International Conference on Data Mining
Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–442
Zhao Y, Wang G, Yu PS, Liu S, Zhang S (2013) Inferring social roles and statuses in social networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Zhou Y, Liu L (2013) Social influence based clustering of heterogeneous information networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Acknowledgments
Sarvenaz Choobdar is funded by an FCT Research Grant (SFRH/BD/72697/2010). Pedro Ribeiro is funded by an FCT Research Grant (SFRH/BPD/81695/2011). This work is partially funded by ERDF, COMPETE, and national funds through FCT within Project FCOMP-01-0124-FEDER-037281.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editors: Toon Calders, Floriana Esposito, Eyke Hüllermeier and Rosa Meo.
Rights and permissions
About this article
Cite this article
Choobdar, S., Ribeiro, P., Parthasarathy, S. et al. Dynamic inference of social roles in information cascades. Data Min Knowl Disc 29, 1152–1177 (2015). https://doi.org/10.1007/s10618-015-0402-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10618-015-0402-5