Modeling and minimizing information distortion in information diffusion through a social network
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It is very common in real life that information distorts during the process of transmission in a social network, which may lead to people’s incorrect comprehension of the information and further poor decision making. In this paper, we study how to model and minimize the distortion of information when it diffuses through a social network. We propose the concept of information authenticity to measure distortion as well as a mathematical model to characterize how information distorts during its diffusion through a social network, and study the optimization problem of maximizing the information authenticity of a social network. In order to solve the problem, we employ a framework of greedy algorithms that was proposed by Ni et al. (Inf Sci 180(13):2514–2527, 2010), which can trade off between optimality and complexity. Finally, we perform experiments to show the greedy algorithms can effectively solve the problem we propose.
KeywordsSocial networks Information distortion Information authenticity Stochastic simulation Greedy algorithm
This work was supported by National Natural Science Foundation of China (Nos. 71471038, 71101027, 71001080, 71171191) and Beijing Higher Education Young Elite Teacher Project (No. YETP0909).
Compliance with ethical standards
Conflict of interest
Yaodong Ni, Liu Ning, Hua Ke, Xiaoyu Ji declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Berger N, Borgs C, Chayes JT, Saberi A (2005) On the spread of viruses on the internet. In: Proceedings of the sixteenth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, pp 301–310Google Scholar
- Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 57–66Google Scholar
- Ebbinghaus H (1886) A supposed law of memory. Mind 42:300-aGoogle Scholar
- Ganesh A, Massoulié L, Towsley D (2005) The effect of network topology on the spread of epidemics. In 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE INFOCOM (2005), vol 2. IEEE, pp 1455–1466Google Scholar
- Hong R, Pan J, Hao S, Wang M, Xue F, Wu X (2014) Image quality assessment based on matching pursuit. Inf Sci 273:196–211Google Scholar
- 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 137–146Google Scholar
- Kempe D, Kleinberg J, Tardos É (2005) Influential nodes in a diffusion model for social networks. In: Proceedings of the thirty-second international colloquium on automata, languages and programming. Springer, pp 1127–1138Google Scholar
- Leskovec J (2007) Stanford large network dataset collection. http://snap.stanford.edu/data/
- Mossel E, Roch S (2007) On the submodularity of influence in social networks. In: Proceedings of the thirty-ninth annual ACM symposium on theory of computing. ACM, pp 128–134Google Scholar
- Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 61–70Google Scholar
- Valente TW (1995) Network models of the diffusion of innovations (quantitative methods in communication series). Hampton Press, New YorkGoogle Scholar